{"jobs":[{"absolute_url":"https://job-boards.greenhouse.io/translucent/jobs/4163709009","data_compliance":[{"type":"gdpr","requires_consent":false,"requires_processing_consent":false,"requires_retention_consent":false,"retention_period":null,"demographic_data_consent_applies":false}],"internal_job_id":4045666009,"location":{"name":"New York"},"metadata":null,"id":4163709009,"updated_at":"2026-03-03T10:26:51-05:00","requisition_id":"7","title":"Deployment Lead","company_name":"Translucent","first_published":"2026-03-02T19:23:23-05:00","language":"en","application_deadline":null,"content":"\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Why Translucent\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;Healthcare providers drive $2.5 trillion in medical expenditures annually — and operate on razor-thin 2–5% margins. Despite these stakes, the finance teams behind these organizations are buried in spreadsheets, manual data pulls, and disconnected systems, spending more time finding and cleaning data than actually using it to make decisions.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;Translucent is changing that. We\u0026#39;re building the agentic AI platform designed exclusively for healthcare finance— giving every finance team, department, service line their own arsenal of AI Agents that run 24/7, understand their specific data, business logic, and workflows.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;Founded in 2024 and backed by GV, NEA, FPV, and Virtue, we\u0026#39;ve already been deployed by healthcare organizations managing over $5 billion in combined revenue. The product-market fit is real, the problem is massive, and we\u0026#39;re just getting started. If you want to work at the intersection of AI and one of the most complex, consequential industries in the world — this is the place.\u0026lt;br\u0026gt;\u0026lt;br\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;p class=\u0026quot;font-claude-response-body break-words whitespace-pre-wrap leading-[1.7]\u0026quot;\u0026gt;\u0026lt;strong\u0026gt;About the Role\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;p class=\u0026quot;font-claude-response-body break-words whitespace-pre-wrap leading-[1.7]\u0026quot;\u0026gt;As a Deployment Lead at Translucent, you\u0026#39;ll own the end-to-end deployment of our AI financial analyst at major health systems. Working closely with Forward-Deployed Engineers, Product, and AI Engineering, you\u0026#39;ll drive customers from signed contract to live platform — navigating complex stakeholders, technical integrations, and the messy realities of healthcare data along the way. This is a client-facing, execution-heavy role that sits at the intersection of project delivery and strategic relationship management — embedded, high-trust, and deeply accountable for customer outcomes.\u0026lt;/p\u0026gt;\n\u0026lt;p class=\u0026quot;font-claude-response-body break-words whitespace-normal leading-[1.7]\u0026quot;\u0026gt;\u0026lt;strong\u0026gt;Responsibilities\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;ul class=\u0026quot;[li_\u0026amp;amp;]:mb-0 [li_\u0026amp;amp;]:mt-1 [li_\u0026amp;amp;]:gap-1 [\u0026amp;amp;:not(:last-child)_ul]:pb-1 [\u0026amp;amp;:not(:last-child)_ol]:pb-1 list-disc flex flex-col gap-1 pl-8 mb-3\u0026quot;\u0026gt;\n\u0026lt;li class=\u0026quot;whitespace-normal break-words pl-2\u0026quot;\u0026gt;Own the full deployment lifecycle — scoping, data integration, configuration, validation, QA, go-live, and post-launch adoption — at enterprise health systems\u0026lt;/li\u0026gt;\n\u0026lt;li class=\u0026quot;whitespace-normal break-words pl-2\u0026quot;\u0026gt;Manage complex stakeholder environments across CFOs, data teams, IT, and clinical operations, aligning diverse teams toward shared deployment goals\u0026lt;/li\u0026gt;\n\u0026lt;li class=\u0026quot;whitespace-normal break-words pl-2\u0026quot;\u0026gt;Partner with Forward-Deployed Engineers, product, and engineering to drive technical workstreams, unblock integration challenges, and close the loop on field observations — you\u0026#39;re a primary voice for what our customers actually need\u0026lt;/li\u0026gt;\n\u0026lt;li class=\u0026quot;whitespace-normal break-words pl-2\u0026quot;\u0026gt;Build client trust in both deterministic and AI-generated outputs — know the difference, communicate it clearly, and design the validation process that gets customers to confident\u0026lt;/li\u0026gt;\n\u0026lt;li class=\u0026quot;whitespace-normal break-words pl-2\u0026quot;\u0026gt;Create structure where there isn\u0026#39;t any: own the project plan, drive accountability on both sides, and escalate proactively when things slip\u0026lt;/li\u0026gt;\n\u0026lt;li class=\u0026quot;whitespace-normal break-words pl-2\u0026quot;\u0026gt;Capture learnings from each deployment and translate them into reusable playbooks, integration templates, and product feedback that make future deployments faster\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;p class=\u0026quot;font-claude-response-body break-words whitespace-normal leading-[1.7]\u0026quot;\u0026gt;\u0026lt;strong\u0026gt;What You Bring\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;ul class=\u0026quot;[li_\u0026amp;amp;]:mb-0 [li_\u0026amp;amp;]:mt-1 [li_\u0026amp;amp;]:gap-1 [\u0026amp;amp;:not(:last-child)_ul]:pb-1 [\u0026amp;amp;:not(:last-child)_ol]:pb-1 list-disc flex flex-col gap-1 pl-8 mb-3\u0026quot;\u0026gt;\n\u0026lt;li class=\u0026quot;whitespace-normal break-words pl-2\u0026quot;\u0026gt;4+ years in a deployment, implementation, or forward-deployed role at an enterprise software or technology company\u0026lt;/li\u0026gt;\n\u0026lt;li class=\u0026quot;whitespace-normal break-words pl-2\u0026quot;\u0026gt;Proven track record owning enterprise client relationships in technically complex, high-stakes environments\u0026lt;/li\u0026gt;\n\u0026lt;li class=\u0026quot;whitespace-normal break-words pl-2\u0026quot;\u0026gt;Strong project execution instincts: you drive workstreams, hold clients accountable for their side, and create structure in ambiguity\u0026lt;/li\u0026gt;\n\u0026lt;li class=\u0026quot;whitespace-normal break-words pl-2\u0026quot;\u0026gt;Technical credibility without being a builder — you can talk data, integrations, and systems fluently, and you know what you don\u0026#39;t know\u0026lt;/li\u0026gt;\n\u0026lt;li class=\u0026quot;whitespace-normal break-words pl-2\u0026quot;\u0026gt;Clear, direct communicator who can present to a CFO and a data engineer on the same day and land with both\u0026lt;/li\u0026gt;\n\u0026lt;li class=\u0026quot;whitespace-normal break-words pl-2\u0026quot;\u0026gt;Fast learner: comfortable ramping on unfamiliar domains, doing your own research, and synthesizing quickly\u0026lt;/li\u0026gt;\n\u0026lt;li class=\u0026quot;whitespace-normal break-words pl-2\u0026quot;\u0026gt;Startup realist: ready to sprint when priorities shift, operate without a playbook, and figure things out independently\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;p class=\u0026quot;font-claude-response-body break-words whitespace-normal leading-[1.7]\u0026quot;\u0026gt;\u0026lt;strong\u0026gt;Bonus Points\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;ul class=\u0026quot;[li_\u0026amp;amp;]:mb-0 [li_\u0026amp;amp;]:mt-1 [li_\u0026amp;amp;]:gap-1 [\u0026amp;amp;:not(:last-child)_ul]:pb-1 [\u0026amp;amp;:not(:last-child)_ol]:pb-1 list-disc flex flex-col gap-1 pl-8 mb-3\u0026quot;\u0026gt;\n\u0026lt;li class=\u0026quot;whitespace-normal break-words pl-2\u0026quot;\u0026gt;Healthcare domain knowledge (revenue cycle, financial operations, payer dynamics)\u0026lt;/li\u0026gt;\n\u0026lt;li class=\u0026quot;whitespace-normal break-words pl-2\u0026quot;\u0026gt;Experience deploying AI or analytics products where building trust in outputs is part of the work\u0026lt;/li\u0026gt;\n\u0026lt;li class=\u0026quot;whitespace-normal break-words pl-2\u0026quot;\u0026gt;Familiarity with common healthcare data systems (EHR, ERP, and related platforms)\u0026lt;/li\u0026gt;\n\u0026lt;li class=\u0026quot;whitespace-normal break-words pl-2\u0026quot;\u0026gt;Background in a high-growth startup or early-stage environment\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;p class=\u0026quot;font-claude-response-body break-words whitespace-pre-wrap leading-[1.7]\u0026quot;\u0026gt;About Translucent Join us in building a world where hospitals and clinics don\u0026#39;t have to choose between financial health and patient health. Translucent automates manual data work so healthcare finance teams can focus on moving the margin — giving hospitals the financial clarity they need to thrive.\u0026lt;/p\u0026gt;\n\u0026lt;p class=\u0026quot;font-claude-response-body break-words whitespace-normal leading-[1.7]\u0026quot;\u0026gt;As a Deployment Lead at Translucent, you\u0026#39;ll own the customer journey from signed contract to realized value, driving deployments that transform how health systems understand and act on their financial performance.\u0026lt;/p\u0026gt;\n\u0026lt;p class=\u0026quot;font-claude-response-body break-words whitespace-normal leading-[1.7]\u0026quot;\u0026gt;Anticipated compensation: $175,000 - $225,000\u0026lt;/p\u0026gt;","departments":[{"id":4015790009,"name":"Delivery","child_ids":[],"parent_id":null}],"offices":[{"id":4015264009,"name":"New York City","location":"New York, New York, United States","child_ids":[],"parent_id":null}]},{"absolute_url":"https://job-boards.greenhouse.io/translucent/jobs/4164088009","data_compliance":[{"type":"gdpr","requires_consent":false,"requires_processing_consent":false,"requires_retention_consent":false,"retention_period":null,"demographic_data_consent_applies":false}],"internal_job_id":4050956009,"location":{"name":"New York City near Union Square"},"metadata":null,"id":4164088009,"updated_at":"2026-05-28T18:22:41-04:00","requisition_id":"10","title":"Healthcare Actuarial Science Domain Expert, Applied AI","company_name":"Translucent","first_published":"2026-03-03T10:41:30-05:00","language":"en","application_deadline":null,"content":"\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Healthcare Actuary, Applied Researcher\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Role Details\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Full-Time\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Office Location — New York City (preferred)\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Why Translucent\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;Healthcare providers drive $2.5 trillion in medical expenditures annually — and operate on razor-thin 2–5% margins. Despite these stakes, the finance teams behind these organizations are buried in spreadsheets, manual data pulls, and disconnected systems, spending more time finding and cleaning data than actually using it to make decisions.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;Translucent is changing that. We\u0026#39;re building the AI-native financial platform designed exclusively for healthcare — giving every finance team, department, and service line their own arsenal of AI Agents that run 24/7, understand their specific data, business logic, and workflows.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;Founded in 2024 and backed by GV, NEA, FPV, and Virtue, we\u0026#39;ve already been deployed by healthcare organizations managing over $5 billion in combined revenue. The product-market fit is real, the problem is massive, and we\u0026#39;re just getting started. If you want to work at the intersection of AI and one of the most complex, consequential industries in the world — this is the place.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Role Overview\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;We are looking for a healthcare actuary with deep expertise in how health systems and medical groups evaluate risk, price contracts, forecast costs, and navigate the shift toward value-based care. We have incredible product-market fit and demand across diverse customer profiles. Executing on this demand requires someone who understands actuarial methodology inside and out — and can translate that expertise into AI-powered systems that scale across hundreds of healthcare organizations.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;What You\u0026#39;ll Do\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;To facilitate the development of these systems, you will:\u0026lt;/p\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Develop and deliver subject-matter expertise in healthcare actuarial science to support AI research — including risk stratification, financial forecasting, payer mix analysis, and reimbursement modeling\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Build forward-looking forecasting models for healthcare organizations: cost trend projections, utilization forecasts, revenue forecasts under different payer and contract scenarios, and budget variance prediction\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Develop frameworks for evaluating value-based care performance, including shared savings/losses, capitation economics, ACO financial benchmarking, and risk corridor analysis\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Model healthcare pricing and risk, including rate-setting assumptions, medical loss ratios, utilization patterns, and population health cost drivers\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Build revenue cycle analytics models covering denial rate drivers, net collection forecasting, contractual adjustment analysis, and payer contract performance\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Work closely with our engineering, product, and design teams to operationalize actuarial logic — including forecasting engines — into production code and AI Agents\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Support risk-bearing entity analysis — helping health systems understand their exposure across commercial, Medicare Advantage, Medicaid managed care, and direct contracting arrangements\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Partner directly with customers to validate assumptions, stress-test forecasts, and translate complex actuarial and risk concepts for finance audiences\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Build proprietary benchmarks and datasets to evaluate models and AI Agents against real-world actuarial tasks — including cost trending, risk scoring, reserve estimation, and contract modeling\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;What You Have\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;ASA or FSA designation (or near-credentialed with a clear path)\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;5–10 years of experience in healthcare actuarial work — health plan pricing, provider risk, Medicare/Medicaid, commercial lines, or value-based care arrangements\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Proven experience building financial forecasts in healthcare: cost trends, utilization projections, revenue modeling, or reserve development\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Deep understanding of risk-based payment models: capitation, shared savings, bundled payments, risk adjustment (HCC/RAF scoring), and stop-loss structures\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Strong command of claims data, utilization metrics, cost of care analytics, and healthcare reimbursement mechanics\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Ability to effectively communicate with a variety of internal and external stakeholders and translate complex actuarial problems between finance, product, and engineering teams\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Ability to define positive outcomes in situations with underspecified success criteria\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Deep intellectual curiosity and eagerness to learn across domains — particularly at the intersection of actuarial science and AI\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Willingness and desire to do work in the trenches — e.g., grading hundreds of model-generated forecasts, breaking down thousands of claims files, stress-testing risk models against real-world standards. Getting AI to do actuary-level healthcare finance work requires a lot of things that look like actuary-level healthcare finance work\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Nice to Have\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Experience on the provider side — working with health system finance teams evaluating risk-bearing contracts, not just payer-side reserving\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Familiarity with SQL, Python, or other data tools\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Background in building scenario-based or Monte Carlo–style forecasting models\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Background in Medicare Advantage bid development, MSSP/ACO REACH benchmarking, or Medicaid managed care rate-setting\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Prior exposure to AI/ML concepts or prompt engineering\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Experience at a high-growth startup\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;p\u0026gt;Anticipated compensation: $175,000 - $250,000 with Equity\u0026lt;/p\u0026gt;","departments":[{"id":4021635009,"name":"Insights","child_ids":[],"parent_id":null}],"offices":[{"id":4015264009,"name":"New York City","location":"New York, New York, United States","child_ids":[],"parent_id":null}]},{"absolute_url":"https://job-boards.greenhouse.io/translucent/jobs/4164094009","data_compliance":[{"type":"gdpr","requires_consent":false,"requires_processing_consent":false,"requires_retention_consent":false,"retention_period":null,"demographic_data_consent_applies":false}],"internal_job_id":4050956009,"location":{"name":"New York City near Union Square"},"metadata":null,"id":4164094009,"updated_at":"2026-05-28T18:22:52-04:00","requisition_id":"10","title":"Healthcare Economics Domain Expert, Applied AI","company_name":"Translucent","first_published":"2026-03-03T10:43:05-05:00","language":"en","application_deadline":null,"content":"\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Healthcare Economist, Applied Researcher\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Role Details\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Full-Time\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Office Location: New York City (preferred)\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Why Translucent\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;Healthcare providers drive $2.5 trillion in medical expenditures annually and operate on razor-thin 2–5% margins. Despite these stakes, the finance teams behind these organizations are buried in spreadsheets, manual data pulls, and disconnected systems, spending more time finding and cleaning data than actually using it to make decisions.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;Translucent is changing that. We\u0026#39;re building the AI-native financial platform designed exclusively for healthcare, giving every finance team, department, and service line their own arsenal of AI Agents that run 24/7, understand their specific data, business logic, and workflows.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;Founded in 2024 and backed by GV, NEA, FPV, and Virtue, we\u0026#39;ve already been deployed by healthcare organizations managing over $5 billion in combined revenue. The product-market fit is real, the problem is massive, and we\u0026#39;re just getting started. If you want to work at the intersection of AI and one of the most complex, consequential industries in the world, this is the place.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Role Overview\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;We are looking for a healthcare economist with deep expertise in revenue cycle management, reimbursement economics, payer-provider dynamics, and the financial operations of enterprise healthcare organizations. We have incredible product-market fit and demand across diverse customer profiles. Executing on this demand requires someone who understands the full lifecycle of healthcare revenue, from charge capture to final payment, and can translate that expertise into AI-powered systems that help finance teams move from reactive reporting to proactive decision-making.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;What You\u0026#39;ll Do\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;To facilitate the development of these systems, you will:\u0026lt;/p\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Develop and deliver subject-matter expertise in healthcare economics and revenue cycle management to support AI research, including reimbursement modeling, denial economics, payer contract analysis, and net revenue optimization\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Build analytical frameworks for the full revenue cycle: charge capture efficiency, coding accuracy and its financial impact, claims submission and adjudication patterns, denial root cause analysis, and collections performance\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Model payer-provider economics, including contract rate analysis, fee schedule benchmarking, allowable vs. billed variance, and the financial impact of payer mix shifts\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Develop frameworks for understanding reimbursement mechanics across Medicare (IPPS, OPPS, physician fee schedule), Medicaid, managed care, and commercial payers\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Analyze the economics of clinical operations, including physician productivity, procedure-level profitability, service line contribution margins, and cost-to-collect ratios\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Work closely with our engineering, product, and design teams to translate healthcare economic logic into production code and AI Agents\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Partner directly with customers to understand their revenue cycle pain points, identify financial leakage, and translate complex reimbursement and operational requirements into technical solutions\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Build proprietary benchmarks and datasets to evaluate models and AI Agents against real-world healthcare finance tasks, including denial rate analysis, days in A/R trending, net collection rate modeling, and payer performance scoring\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;What You Have\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;5–10 years of experience in healthcare economics, revenue cycle management, healthcare consulting, or an equivalent function within a health system, medical group, or payer organization\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Deep understanding of healthcare reimbursement: how providers get paid, what drives variation in payment, and where revenue leaks across the cycle\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Strong command of revenue cycle KPIs: denial rates, days in A/R, clean claim rates, net collection rates, cost to collect, and how these metrics connect to financial performance\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Familiarity with Medicare and Medicaid reimbursement methodologies (DRGs, APCs, RBRVS, etc.) and commercial contract structures\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Experience analyzing claims data, remittance files, charge masters, or payer contracts to identify financial trends and opportunities\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Ability to effectively communicate with a variety of internal and external stakeholders and translate complex healthcare economic problems between finance, product, and engineering teams\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Ability to define positive outcomes in situations with underspecified success criteria\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Deep intellectual curiosity and eagerness to learn across domains, particularly at the intersection of healthcare economics and AI\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Willingness and desire to do work in the trenches. Grading hundreds of model-generated denial analyses, breaking down thousands of remittance records, stress-testing reimbursement models against real-world payer behavior. Getting AI to do analyst-level revenue cycle work requires a lot of things that look like analyst-level revenue cycle work\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Nice to Have\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Experience working inside a health system revenue cycle or finance department, not just consulting to one\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Familiarity with SQL, Python, or other data tools\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Background in healthcare price transparency, chargemaster optimization, or payer contract negotiation strategy\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Experience with value-based care payment models and their impact on revenue cycle operations\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Knowledge of healthcare regulatory economics: No Surprises Act, site-of-service differentials, 340B, etc.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Prior exposure to AI/ML concepts or prompt engineering\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Experience at a high-growth startup\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;p\u0026gt;Anticipated compensation: $175,000 - $250,000 with Equity\u0026lt;/p\u0026gt;","departments":[{"id":4021635009,"name":"Insights","child_ids":[],"parent_id":null}],"offices":[{"id":4015264009,"name":"New York City","location":"New York, New York, United States","child_ids":[],"parent_id":null}]},{"absolute_url":"https://job-boards.greenhouse.io/translucent/jobs/4164085009","data_compliance":[{"type":"gdpr","requires_consent":false,"requires_processing_consent":false,"requires_retention_consent":false,"retention_period":null,"demographic_data_consent_applies":false}],"internal_job_id":4050956009,"location":{"name":"New York City near Union Square"},"metadata":null,"id":4164085009,"updated_at":"2026-05-28T18:23:02-04:00","requisition_id":"10","title":"Healthcare FP\u0026A Domain Expert, Applied AI","company_name":"Translucent","first_published":"2026-03-03T10:39:25-05:00","language":"en","application_deadline":null,"content":"\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Role Details\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Full-Time\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Office Location - New York City (preferred)\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Why Translucent\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;Healthcare providers drive $2.5 trillion in medical expenditures annually — and operate on razor-thin 2–5% margins. Despite these stakes, the finance teams behind these organizations are buried in spreadsheets, manual data pulls, and disconnected systems, spending more time finding and cleaning data than actually using it to make decisions.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;Translucent is changing that. We\u0026#39;re building the agentic AI platform designed exclusively for healthcare finance— giving every finance team, department, service line their own arsenal of AI Agents that run 24/7, understand their specific data, business logic, and workflows.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;Founded in 2024 and backed by GV, NEA, FPV, and Virtue, we\u0026#39;ve already been deployed by healthcare organizations managing over $5 billion in combined revenue. The product-market fit is real, the problem is massive, and we\u0026#39;re just getting started. If you want to work at the intersection of AI and one of the most complex, consequential industries in the world — this is the place.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Role Overview\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;We are looking for an experienced healthcare FP\u0026amp;amp;A person with a strong understanding of how finance teams at enterprise healthcare provider organizations (medical groups, health systems) plan, forecast, analyze, and report on business performance. We have incredible product-market fit and demand across diverse customer profiles. Executing on this demand requires a deep understanding of our customers\u0026#39; financial planning workflows and how those workflows can be accelerated by AI systems.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;What You\u0026#39;ll Do\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;To facilitate the development of these systems, you will:\u0026lt;/p\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Develop and deliver subject-matter expertise in financial planning \u0026amp;amp; analysis to support AI research\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Work closely with our engineering, product, and design teams to define and develop AI systems for FP\u0026amp;amp;A workflows\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Build proprietary benchmarks and datasets to evaluate models and AI Agents against real-world FP\u0026amp;amp;A tasks — including forecasting, variance analysis, scenario modeling, board reporting, and more\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Partner directly with customers to understand their planning cycles, identify pain points, and translate complex financial and business requirements into technical solutions\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;What You Have\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;7 years of experience in FP\u0026amp;amp;A, corporate finance, investment banking, or an equivalent finance function — ideally with exposure to budgeting, forecasting, financial modeling, and management reporting\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Strong proficiency with financial models, spreadsheets, and common FP\u0026amp;amp;A tools (e.g., Excel, Google Sheets, Anaplan, Adaptive, NetSuite, Strata or similar)\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Demonstrated ability to deliver high-quality financial analysis on demanding deadlines\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Ability to effectively communicate with a variety of internal and external stakeholders and translate complex problems between finance, product, and engineering teams\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Ability to define positive outcomes in situations with underspecified success criteria\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Deep intellectual curiosity and eagerness to learn across domains — particularly at the intersection of finance and AI\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Willingness and desire to do work in the trenches — e.g., grading hundreds of model-generated forecasts, breaking down thousands of financial documents, stress-testing outputs against real-world standards. Getting AI to do analyst-level FP\u0026amp;amp;A work requires a lot of things that look like analyst-level FP\u0026amp;amp;A work\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Nice to Have\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Experience at a high-growth startup\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Familiarity with SQL, Python, or other data tools\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Prior exposure to AI/ML concepts or prompt engineering\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;p\u0026gt;Anticipated compensation: $175,000 - $250,000 and Equity\u0026lt;/p\u0026gt;","departments":[{"id":4021635009,"name":"Insights","child_ids":[],"parent_id":null}],"offices":[{"id":4015264009,"name":"New York City","location":"New York, New York, United States","child_ids":[],"parent_id":null}]},{"absolute_url":"https://job-boards.greenhouse.io/translucent/jobs/4246634009","data_compliance":[{"type":"gdpr","requires_consent":false,"requires_processing_consent":false,"requires_retention_consent":false,"retention_period":null,"demographic_data_consent_applies":false}],"education":"education_required","internal_job_id":4144052009,"location":{"name":"New York City"},"metadata":null,"id":4246634009,"updated_at":"2026-05-31T05:55:33-04:00","requisition_id":"14","title":"Senior Data Engineer","company_name":"Translucent","first_published":"2026-05-27T12:57:00-04:00","language":"en","application_deadline":null,"content":"\u0026lt;h2\u0026gt;\u0026lt;strong\u0026gt;Why Translucent\u0026lt;/strong\u0026gt;\u0026lt;/h2\u0026gt;\n\u0026lt;p\u0026gt;Healthcare providers drive $2.5 trillion in medical expenditures annually — and operate on razor-thin 2–5% margins. Despite these stakes, the finance teams behind these organizations are buried in spreadsheets, manual data pulls, and disconnected systems, spending more time finding and cleaning data than actually using it to make decisions.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;Translucent is changing that. We\u0026#39;re building the agentic AI platform designed exclusively for healthcare finance— giving every finance team, department, service line their own arsenal of AI Agents that run 24/7, understand their specific data, business logic, and workflows.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;Founded in 2024 and backed by GV, NEA, FPV, and Virtue, we\u0026#39;ve already been deployed by healthcare organizations managing over $5 billion in combined revenue. The product-market fit is real, the problem is massive, and we\u0026#39;re just getting started. If you want to work at the intersection of AI and one of the most complex, consequential industries in the world — this is the place.\u0026lt;/p\u0026gt;\n\u0026lt;h3\u0026gt;About the role\u0026lt;/h3\u0026gt;\n\u0026lt;p\u0026gt;We’re hiring a Senior Forward Deployed Engineer with a data engineering focus to join our team in New York. You will sit at the intersection of our customers\u0026#39; financial and clinical data and the agentic AI systems we are building on top of it. You will build and maintain the data integration interfaces, pipelines, and semantic layers that make healthcare data legible to our products, and you will partner closely with platform engineers to keep our core data systems fast, correct, and well-tested.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;This is a high-ownership role. You will work directly with customers to understand their source-of-truth financial and clinical models, then translate that understanding into durable, well-modeled data systems.\u0026lt;/p\u0026gt;\n\u0026lt;h3\u0026gt;What you\u0026#39;ll do\u0026lt;/h3\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Build and maintain abstracted data integration interfaces for healthcare data.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Capture and understand the source-of-truth financial and clinical record models used by our customers. Potentially go on-site with customers to capture business logic.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Optimize data ingestion and transformation pipelines for correctness, performance, and cost.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Build semantic understanding into our core data systems so they can power agentic AI workflows.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Partner with platform engineers to build and maintain the core data systems our products depend on.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Validate data pipeline correctness and build rigor into our integration test suites at every opportunity.\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;h2\u0026gt;\u0026lt;strong\u0026gt;What we\u0026#39;re looking for\u0026lt;/strong\u0026gt;\u0026lt;/h2\u0026gt;\n\u0026lt;h3\u0026gt;\u0026lt;strong\u0026gt;Must-haves\u0026lt;/strong\u0026gt;\u0026lt;/h3\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Strong SQL optimization experience in a columnar data warehouse (BigQuery, Snowflake, Redshift, or similar), or Databricks.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Strong Python skills, including hands-on experience with pandas (must have) and the broader scientific Python ecosystem. Strong preference to use types where possible, we use pydantic liberally. DBT experience preferred.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Strong grounding in the fundamentals of computation, logic, and algorithms.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Solid data modeling instincts: comfortable designing schemas, reasoning about normalization vs. denormalization, and modeling complex domain entities.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Experience with orchestration tools (\u0026lt;em\u0026gt;e.g.\u0026lt;/em\u0026gt; Prefect, Airflow, Dagster, Fivetran, Airbyte) etc.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;3-5+ years of post-university industry experience\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;h3\u0026gt;\u0026lt;strong\u0026gt;Nice-to-haves\u0026lt;/strong\u0026gt;\u0026lt;/h3\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Hands-on experience with GCP (BigQuery, GCS, Spanner, IAM, etc.).\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Prior exposure to healthcare data (claims, EHR, financial systems) or other regulated/complex domains.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Experience working directly with customers or stakeholders in a forward-deployed, integrations, or solutions engineering capacity.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Golang experience.\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;h3\u0026gt;\u0026lt;strong\u0026gt;Education\u0026lt;/strong\u0026gt;\u0026lt;/h3\u0026gt;\n\u0026lt;p\u0026gt;Bachelor\u0026#39;s degree (or higher) in a quantitative discipline: computer science, mathematics, statistics, physical or life sciences, engineering.\u0026lt;/p\u0026gt;\n\u0026lt;h3\u0026gt;\u0026lt;strong\u0026gt;Location\u0026lt;/strong\u0026gt;\u0026lt;/h3\u0026gt;\n\u0026lt;p\u0026gt;Union Square, New York City.\u0026lt;strong\u0026gt; In-office 4 days per week.\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;Anticipated compensation: $200,000 - $250,000 with Equity\u0026lt;/p\u0026gt;","departments":[{"id":4015786009,"name":"Engineering","child_ids":[],"parent_id":null}],"offices":[{"id":4015264009,"name":"New York City","location":"New York, New York, United States","child_ids":[],"parent_id":null}]},{"absolute_url":"https://job-boards.greenhouse.io/translucent/jobs/4260194009","data_compliance":[{"type":"gdpr","requires_consent":false,"requires_processing_consent":false,"requires_retention_consent":false,"retention_period":null,"demographic_data_consent_applies":false}],"education":"education_required","internal_job_id":4152239009,"location":{"name":"New York City"},"metadata":null,"id":4260194009,"updated_at":"2026-06-01T15:23:47-04:00","requisition_id":"15","title":"Senior Platform Engineer","company_name":"Translucent","first_published":"2026-05-27T13:03:55-04:00","language":"en","application_deadline":null,"content":"\u0026lt;h2\u0026gt;\u0026lt;strong\u0026gt;Why Translucent\u0026lt;/strong\u0026gt;\u0026lt;/h2\u0026gt;\n\u0026lt;p\u0026gt;Healthcare providers drive $2.5 trillion in medical expenditures annually — and operate on razor-thin 2–5% margins. Despite these stakes, the finance teams behind these organizations are buried in spreadsheets, manual data pulls, and disconnected systems, spending more time finding and cleaning data than actually using it to make decisions.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;Translucent is changing that. We\u0026#39;re building the agentic AI platform designed exclusively for healthcare finance— giving every finance team, department, service line their own arsenal of AI Agents that run 24/7, understand their specific data, business logic, and workflows.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;Founded in 2024 and backed by GV, NEA, FPV, and Virtue, we\u0026#39;ve already been deployed by healthcare organizations managing over $5 billion in combined revenue. The product-market fit is real, the problem is massive, and we\u0026#39;re just getting started. If you want to work at the intersection of AI and one of the most complex, consequential industries in the world — this is the place.\u0026lt;/p\u0026gt;\n\u0026lt;h3\u0026gt;About the role\u0026lt;/h3\u0026gt;\n\u0026lt;p\u0026gt;We’re hiring a Senior Platform Engineer focussed on supporting and enabling data and AI engineering in New York. You will build and maintain the foundational platform technology that enables our data and agentic AI systems and that form our product. You will build and help maintain infrastructure primarily in GCP and you will partner closely with other platform engineers and product engineers in keeping our core systems fast, correct, secure and well-tested.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;This is a high-ownership role. You will work directly with a large group of product engineers in enabling our product teams to move fast.\u0026lt;/p\u0026gt;\n\u0026lt;h3\u0026gt;What you\u0026#39;ll do\u0026lt;/h3\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Build and maintain well-tested applications in Python, Typescript and golang.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Build and maintain kubernetes (GKE) deployments and workloads with our preferred gitops approach. We use github, ArgoCD and Kargo.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Manage IaC on GCP.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Build, help deploy and optimize applications using MongoDB Atlas, PostgresQL and Spanner as backing state stores.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Document processes and work to optimize LLM-assisted coding workflows, including optimizing code review strategies.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Partner with product engineers to support rapid feature development.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Validate software is correct and secure.\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;h2\u0026gt;\u0026lt;strong\u0026gt;What we\u0026#39;re looking for\u0026lt;/strong\u0026gt;\u0026lt;/h2\u0026gt;\n\u0026lt;h3\u0026gt;\u0026lt;strong\u0026gt;Must-haves\u0026lt;/strong\u0026gt;\u0026lt;/h3\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Experience with GCP\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Strong Kubernetes experience, preferably using GKE (autopilot \u0026amp;amp; standard), strong understanding of the mechanics of workload scaling in GKE\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Strong experience with relational databases, managing migrations and experience with Mongo.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Strong Python, golang and Typescript programming experience.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Strong experience with IaC tooling (OpenTofu) - nice to have Atlantis.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Strong grounding in the fundamentals of computation, logic, and algorithms.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;5+ years of post-university industry experience\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;h3\u0026gt;\u0026lt;strong\u0026gt;Nice-to-haves\u0026lt;/strong\u0026gt;\u0026lt;/h3\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Prior experience in a heavily regulated domain like finance or healthtech.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Experience with Vertex (Gemini Enterprise Agent Platform)\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Experience building and deploying agentic systems\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Experience building or maintaining Kubernetes operators\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Experience with honeycomb.io\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Experience with Backstage\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;h3\u0026gt;\u0026lt;strong\u0026gt;Education\u0026lt;/strong\u0026gt;\u0026lt;/h3\u0026gt;\n\u0026lt;p\u0026gt;Bachelor\u0026#39;s degree (or higher) in a quantitative discipline: computer science, mathematics, statistics, physical or life sciences, engineering.\u0026lt;/p\u0026gt;\n\u0026lt;h3\u0026gt;\u0026lt;strong\u0026gt;Location\u0026lt;/strong\u0026gt;\u0026lt;/h3\u0026gt;\n\u0026lt;p\u0026gt;Union Square, New York City.\u0026lt;strong\u0026gt; In-office 4 days per week.\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;Anticipated compensation: $200,000 - $250,000 with Equity\u0026lt;/p\u0026gt;","departments":[{"id":4015786009,"name":"Engineering","child_ids":[],"parent_id":null}],"offices":[{"id":4015264009,"name":"New York City","location":"New York, New York, United States","child_ids":[],"parent_id":null}]}],"meta":{"total":6}}