{"jobs":[{"absolute_url":"https://job-boards.greenhouse.io/wizardcommerce/jobs/6002368004","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":5157454004,"location":{"name":"Remote - USA"},"metadata":null,"id":6002368004,"updated_at":"2026-05-21T05:55:21-04:00","requisition_id":"121","title":"AI Applied Scientist","company_name":"Wizard","first_published":"2026-05-21T05:55:21-04:00","language":"en","application_deadline":null,"content":"\u0026lt;h2\u0026gt;\u0026lt;strong\u0026gt;About Wizard\u0026lt;/strong\u0026gt;\u0026lt;/h2\u0026gt;\n\u0026lt;p\u0026gt;Wizard is the top-performing AI Shopping Agent, delivering the best products from across the web with unmatched accuracy, quality, and trust.\u0026lt;/p\u0026gt;\n\u0026lt;h2\u0026gt;\u0026lt;strong\u0026gt;The Role\u0026lt;/strong\u0026gt;\u0026lt;/h2\u0026gt;\n\u0026lt;p\u0026gt;We’re looking for an Applied Scientist to own how we measure, understand, and improve the accuracy of our AI agent. This role sits at the intersection of applied ML, evaluation science, and product. You’ll define what “good” looks like for our agent, build the systems to measure it, and lead the science work to improve it, including fine-tuning the LLM judges that power our evaluation pipeline.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;You’ll partner with ML Engineering and AI Engineering. What you will do is bring scientific rigor to the most important question at Wizard: is our agent getting better, and how do we know?\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;This is a foundational hire on our science team. Evaluation is the starting point, and the role is scoped to grow into broader applied science work as the surface area of the agent expands (recommendations, personalization, ranking, multimodal, conversational understanding).\u0026lt;/p\u0026gt;\n\u0026lt;h2\u0026gt;\u0026lt;strong\u0026gt;What You’ll Do\u0026lt;/strong\u0026gt;\u0026lt;/h2\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Define and evolve accuracy metrics across the full shopping experience (retrieval, ranking, recommendations, outcomes)\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Design and run experiments to measure improvements and regressions\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Build and maintain evaluation datasets, benchmarks, and scoring frameworks\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Improve the LLM judges that power our evaluation pipeline: prompting, calibration, and fine-tuning where it matters\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Translate ambiguous product questions into clear, measurable hypotheses and analysis\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Partner with ML Engineers to validate model changes and guide iteration\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Identify failure modes and edge cases, and drive improvements through data\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Make agent performance visible, trusted, and actionable across product and engineering\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;h3\u0026gt;First 3 months\u0026lt;/h3\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Go deep on the agent, the current eval pipeline, and the metrics we use today\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Audit existing accuracy metrics and benchmarks; identify gaps, blind spots, and signals that aren’t trustworthy\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Build relationships with ML, AI Engineering, and Product\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Ship one quick win: a missing benchmark, an improved metric, or a fix to a misleading signal\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Establish a baseline view of agent performance the team can rally around\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;h3\u0026gt;Months 3 to 6\u0026lt;/h3\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Own the evaluation framework: datasets, metrics, scoring, reporting, both offline and online\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Drive measurable improvements to LLM judge quality (calibration, fine-tuning where appropriate)\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Run experiments that influence at least one significant model or product change\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Stand up automated evaluation the team trusts before and after every launch\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Build dashboards and reporting that make agent performance legible to leadership\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;h3\u0026gt;Beyond 6 months\u0026lt;/h3\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Lead applied science work on the next frontier as the agent grows: multi-turn evaluation, multimodal, personalization, ranking quality, conversational understanding\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Influence team-level strategy on what we measure, what we improve, and why\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Mentor and help grow the science function as it expands\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;h3\u0026gt;\u0026lt;strong\u0026gt;What Success Looks Like\u0026lt;/strong\u0026gt;\u0026lt;/h3\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Clear, trusted accuracy metrics are consistently used across product and engineering\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;A robust automated evaluation framework for both offline and live experiments\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Model and product changes are consistently measured before and after launch\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Demonstrable improvements in LLM judge quality and eval coverage\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Science leadership that informs what we build, not just whether it works\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;h3\u0026gt;\u0026lt;strong\u0026gt;Career Growth\u0026lt;/strong\u0026gt;\u0026lt;/h3\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Depth track: become the org’s authority on AI evaluation: eval strategy, judge models, agent benchmarking\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Breadth track: expand into other applied science problems (recommendations, personalization, ranking, multimodal, conversational understanding) as those areas come online\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Leadership track: Senior / Staff Applied Scientist, with technical leadership across the science function\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;As the agent gets more capable, the science problems get richer\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;h2\u0026gt;\u0026lt;strong\u0026gt;Ideal Background\u0026lt;/strong\u0026gt;\u0026lt;/h2\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;5+ years in Applied ML, AI Research, or Applied Science (PhD or equivalent depth strongly preferred)\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Hands-on experience evaluating modern AI/ML systems: LLMs, agents, ranking, or recommendations\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Direct experience with LLM-based systems: judge models, RAG, prompt engineering, fine-tuning, RLHF, or similar\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Strong experimentation foundations: A/B testing, causal inference, statistical rigor\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Proven ability to operate in ambiguity: defining problems, not just solving pre-defined ones\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Clear, structured communication that influences across ML, engineering, and product\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;h3\u0026gt;\u0026lt;strong\u0026gt;Compensation \u0026amp;amp; Benefits\u0026lt;/strong\u0026gt;\u0026lt;/h3\u0026gt;\n\u0026lt;p\u0026gt;The expected base salary range for this role is $225,000 - $280,000 USD, and will vary based on skills, experience, role level, and geographic location. Final compensation will be determined by considering these factors alongside overall role scope and responsibilities.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;In addition to base salary, Wizard offers:\u0026lt;/p\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Equity in the form of stock options\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Medical, dental, and vision coverage\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;401(k) plan\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Flexible PTO and company holidays\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Fully remote work within the United States\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Periodic company offsites and team gatherings\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;p\u0026gt;Wizard is committed to fair, transparent, and competitive compensation practices.\u0026lt;/p\u0026gt;","departments":[{"id":4043640004,"name":"AI \u0026 Machine Learning","child_ids":[],"parent_id":4043638004}],"offices":[{"id":4026552004,"name":"Remote","location":"Remote","child_ids":[],"parent_id":null}]},{"absolute_url":"https://job-boards.greenhouse.io/wizardcommerce/jobs/6012596004","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":5161699004,"location":{"name":"New York, NY "},"metadata":null,"id":6012596004,"updated_at":"2026-06-03T12:33:38-04:00","requisition_id":"122","title":"Lifecycle Marketing Manager","company_name":"Wizard","first_published":"2026-06-03T12:33:38-04:00","language":"en","application_deadline":null,"content":"\u0026lt;h2\u0026gt;\u0026lt;strong\u0026gt;About Wizard\u0026lt;/strong\u0026gt;\u0026lt;/h2\u0026gt;\n\u0026lt;p\u0026gt;Wizard is the top-performing AI Shopping Agent, delivering the best products from across the web with unmatched accuracy, quality, and trust.\u0026lt;/p\u0026gt;\n\u0026lt;h2\u0026gt;\u0026lt;strong\u0026gt;The Role\u0026lt;/strong\u0026gt;\u0026lt;/h2\u0026gt;\n\u0026lt;p\u0026gt;We\u0026#39;re hiring a Lifecycle Marketer to own how we communicate with our users after they walk in the door. We have the some of the basics in place but this is largely a build role. You\u0026#39;ll take what exists, assess what\u0026#39;s working, and build the lifecycle program that turns new users into habitual ones.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;You\u0026#39;ll report into our Director of Growth Marketing and sit at the heart of our marketing team, working closely with product and creative to build the infrastructure that makes our path to 1M users sustainable.\u0026lt;/p\u0026gt;\n\u0026lt;h2\u0026gt;\u0026lt;strong\u0026gt;What You’ll Do\u0026lt;/strong\u0026gt;\u0026lt;/h2\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Assess our current email, SMS, and push setup and develop a full lifecycle roadmap covering onboarding, activation, engagement, re-engagement, and retention.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Build, test, and optimize lifecycle campaigns across email, SMS, and push notifications. You’re hands-on in the tools, not just directing from a distance.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Own the critical first-week experience — the sequence of communications that turns a new signup into an activated, returning user.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Write high-quality lifecycle copy across channels (email, SMS, push), maintaining Wizard’s premium, product-led voice.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Design and build scalable email templates and systems that can be reused, iterated on, and improved over time.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Build smarter audience segments and develop more personalized messaging as our user base and data maturity grows.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Leverage dynamic content and event-based triggers to personalize messaging (e.g., product categories viewed, queries made, intent signals).\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Run a disciplined testing program across subject lines, send times, messaging, sequencing, and channel mix. You make decisions with data and document what you learn.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Partner with product to align key moments with lifecycle triggers, and with creative to ensure everything looks and feels like Wizard.\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;What Success Looks like\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Month 3: Completed a full lifecycle audit and launched a rebuilt onboarding system across email, SMS, and push, with testing and scalable templates in place.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Month 6: Lifecycle is driving measurable gains in activation and retention, with coordinated email + SMS programs and live behavior based personalization.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Month 12: Built a mature, dynamic lifecycle system that is a core driver of retention and engagement at scale.\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;h2\u0026gt;\u0026lt;strong\u0026gt;Ideal Background\u0026lt;/strong\u0026gt;\u0026lt;/h2\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;3-5 years of lifecycle, CRM, or retention marketing experience at a consumer tech company or high growth startup\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Hands on experience with a major lifecycle or CRM platform such as Braze, Iterable, Klaviyo, or similar\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Experience building and scaling SMS programs alongside email (compliance, deliverability, sequencing)\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Strong instincts on messaging, sequencing, and user psychology, you understand how to move someone from curious to committed\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;You have a strong analytical mind and are comfortable pulling your own data, building reports, and letting results drive decisions\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Strong writing skills with a high bar for both clarity and tone\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;h3\u0026gt;\u0026lt;strong\u0026gt;Compensation \u0026amp;amp; Benefits\u0026lt;/strong\u0026gt;\u0026lt;/h3\u0026gt;\n\u0026lt;p\u0026gt;The expected base salary range for this role is $125,000 - $175,000 and will vary based on skills, experience, role level, and geographic location. Final compensation will be determined by considering these factors alongside overall role scope and responsibilities.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;In addition to base salary, Wizard offers:\u0026lt;/p\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Equity in the form of stock options\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Medical, dental, and vision coverage\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;401(k) plan\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Flexible PTO and company holidays\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Fully remote work within the United States\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Periodic company offsites and team gatherings\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;p\u0026gt;Wizard is committed to fair, transparent, and competitive compensation practices.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;br\u0026gt;\u0026lt;br\u0026gt;\u0026lt;/p\u0026gt;","departments":[{"id":4096360004,"name":"Marketing","child_ids":[],"parent_id":null}],"offices":[{"id":4026552004,"name":"Remote","location":"Remote","child_ids":[],"parent_id":null}]},{"absolute_url":"https://job-boards.greenhouse.io/wizardcommerce/jobs/5835660004","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":5067883004,"location":{"name":"Remote - USA"},"metadata":null,"id":5835660004,"updated_at":"2026-03-24T10:38:22-04:00","requisition_id":"112","title":"Machine Learning Engineer - Relevance \u0026 Learning Systems","company_name":"Wizard","first_published":"2026-03-24T09:56:10-04:00","language":"en","application_deadline":null,"content":"\u0026lt;h2\u0026gt;\u0026lt;strong\u0026gt;About Wizard\u0026lt;/strong\u0026gt;\u0026lt;/h2\u0026gt;\n\u0026lt;p\u0026gt;Wizard is the top-performing AI Shopping Agent, delivering the best products from across the web with unmatched accuracy, quality, and trust.\u0026lt;/p\u0026gt;\n\u0026lt;h2\u0026gt;\u0026lt;strong\u0026gt;The Role\u0026lt;/strong\u0026gt;\u0026lt;/h2\u0026gt;\n\u0026lt;p\u0026gt;We’re looking for a Machine Learning Engineer to design and build feedback driven learning systems that improve our AI agent over time. This is not a traditional RL research role, we’re focused on building systems that learn from real user behavior and improve production. You’ll be working at the intersection of a live conversational agent and real shopping behavior – the feedback signal quality here is unusually rich compared to traditional search.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;You’ll focus on turning user interactions into learning signals, designing practical feedback loops and shipping systems that continuously improve real world outcomes.\u0026lt;/p\u0026gt;\n\u0026lt;h2\u0026gt;\u0026lt;strong\u0026gt;What You’ll Do\u0026lt;/strong\u0026gt;\u0026lt;/h2\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Build and productionize feedback loops that improve agent performance over time\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Build the evaluation infrastructure – offline metrics, regression suites, and experiment analysis\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Own the signal pipelines end-to-end: instrument events, build clean labeled datasets, and translate user behaviors into reliable learning signals\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Design lightweight reinforcement learning / bandit-style approaches where appropriate\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Partner closely with product and engineering to define success metrics and optimize for them\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Design and analyze experiments that validate whether learning system changes actually improve real outcomes\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Improve ranking, recommendations and decision making within the agent\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Iterate quickly: Ship → measure → learn → improve\u0026amp;nbsp;\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;What Success Looks like\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;You ship quickly and drive measurable improvements in core product metrics\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;You turn noisy user behavior into reliable learning signals that improve the agent over time\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;You own systems end to end and operate comfortably in production\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;h2\u0026gt;\u0026lt;strong\u0026gt;Ideal Background\u0026lt;/strong\u0026gt;\u0026lt;/h2\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;5-8 years hands on experience building and shipping ML systems\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Bachelor’s or Master\u0026#39;s degree in computer science\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Experience shipping ML systems to production and have worked on recommendation systems, ranking, personalization or optimization problems\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Deep knowledge in Python and model ML tooling\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Pragmatic: you choose simple, effective solutions over theoretically perfect ones\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;h3\u0026gt;\u0026lt;strong\u0026gt;Compensation \u0026amp;amp; Benefits\u0026lt;/strong\u0026gt;\u0026lt;/h3\u0026gt;\n\u0026lt;p\u0026gt;The expected base salary range for this role is $225,000 - $280,000 USD, and will vary based on skills, experience, role level, and geographic location. Final compensation will be determined by considering these factors alongside overall role scope and responsibilities.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;In addition to base salary, Wizard offers:\u0026lt;/p\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Equity in the form of stock options\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Medical, dental, and vision coverage\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;401(k) plan\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Flexible PTO and company holidays\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Fully remote work within the United States\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Periodic company offsites and team gatherings\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;p\u0026gt;Wizard is committed to fair, transparent, and competitive compensation practices.\u0026lt;/p\u0026gt;","departments":[{"id":4043640004,"name":"AI \u0026 Machine Learning","child_ids":[],"parent_id":4043638004}],"offices":[{"id":4026552004,"name":"Remote","location":"Remote","child_ids":[],"parent_id":null}]},{"absolute_url":"https://job-boards.greenhouse.io/wizardcommerce/jobs/5837279004","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":5068710004,"location":{"name":"Remote - USA"},"metadata":null,"id":5837279004,"updated_at":"2026-06-03T10:43:17-04:00","requisition_id":"115","title":"Senior Machine Learning Engineer (Inference Platform)","company_name":"Wizard","first_published":"2026-03-25T15:20:51-04:00","language":"en","application_deadline":null,"content":"\u0026lt;h2\u0026gt;About Wizard AI\u0026lt;/h2\u0026gt;\n\u0026lt;p class=\u0026quot;isSelectedEnd\u0026quot;\u0026gt;At Wizard AI, we’re building the top-performing AI Shopping Agent that delivers the best products from across the web with unmatched accuracy, quality, and trust. Our ML models power the core of our platform, and we’re looking for a \u0026lt;strong\u0026gt;Senior Machine Learning Engineer\u0026lt;/strong\u0026gt; to own how they run in production reliably, efficiently, and at scale.\u0026lt;/p\u0026gt;\n\u0026lt;h2\u0026gt;The Role\u0026lt;/h2\u0026gt;\n\u0026lt;p class=\u0026quot;isSelectedEnd\u0026quot;\u0026gt;As a \u0026lt;strong\u0026gt;Senior ML Engineer on our Inference Platform\u0026lt;/strong\u0026gt;, you’ll own the end-to-end lifecycle of production ML serving systems from model packaging and deployment to monitoring, optimization, and scaling. This is not a traditional MLOps role focused solely on pipelines and tooling. You’ll be responsible for the inference infrastructure powering a live conversational shopping agent, operating multiple specialized serving engines under real-world production load.\u0026lt;/p\u0026gt;\n\u0026lt;p class=\u0026quot;isSelectedEnd\u0026quot;\u0026gt;You’ll own critical decisions around serving architecture, performance, reliability, and scalability, working closely with ML Engineers, Data teams, Product, and DevOps to ensure models move seamlessly from experimentation into high-performance production systems.\u0026lt;/p\u0026gt;\n\u0026lt;h2\u0026gt;What You\u0026#39;ll Do\u0026lt;/h2\u0026gt;\n\u0026lt;ul data-spread=\u0026quot;false\u0026quot;\u0026gt;\n\u0026lt;li\u0026gt;Own and evolve our multi-engine inference platform, supporting a variety of model types and serving requirements.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Build and improve production ML pipelines — taking models from experimentation to reliable, high-throughput serving.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Define and implement model versioning, rollout, rollback, and lifecycle management strategies that ensure reproducibility and operational reliability.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Define and enforce serving-layer SLAs, including latency, availability, GPU utilization, Time-to-First-Token (TTFT), and Inter-Token Latency (ITL).\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Build observability, monitoring, alerting, and operational tooling for production inference systems.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Apply software engineering best practices, including testing, CI/CD integration, and reproducibility across ML workflows.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Optimize inference performance through efficient resource utilization, hardware-aware serving strategies, and cost-conscious infrastructure design.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Ensure ML serving systems are secure, scalable, and operationally resilient.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Partner with ML, Data, Product, and DevOps teams to turn ideas into production systems, driving the technical decisions on serving and scale.\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;h2\u0026gt;What We\u0026#39;re Looking For\u0026lt;/h2\u0026gt;\n\u0026lt;ul data-spread=\u0026quot;false\u0026quot;\u0026gt;\n\u0026lt;li\u0026gt;Bachelor\u0026#39;s or Master\u0026#39;s degree in Computer Science, Data Science, Engineering, or a related field, or equivalent practical experience.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;5–8+ years of experience in Software Engineering, ML Engineering, Platform Engineering, or Infrastructure Engineering, with direct ownership of production ML serving systems.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Hands-on experience running an LLM serving engine (vLLM, TGI, TensorRT-LLM, or SGLang) in production under real load — not just managed or hosted endpoints.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Strong Python skills and software engineering fundamentals, combined with deep systems and infrastructure knowledge.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Experience with cloud platforms such as AWS, GCP, or Azure, and familiarity with ML lifecycle tooling, experimentation platforms, and model registries.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Strong grasp of inference performance — continuous batching, KV-cache and GPU-memory behavior, quantization, and CPU-versus-GPU bottlenecks — with the instinct to profile before tuning.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Experience serving heterogeneous workloads, including LLMs, embedding models, and extraction models, each with distinct latency, throughput, and scaling requirements.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Demonstrated ability to balance latency, throughput, reliability, and infrastructure cost while operating production-scale ML systems.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Experience in high-growth startup environments and comfort operating in fast-moving, evolving technical landscapes.\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;h2\u0026gt;What Success Looks Like\u0026lt;/h2\u0026gt;\n\u0026lt;h3\u0026gt;Reliable, Scalable Inference Systems\u0026lt;/h3\u0026gt;\n\u0026lt;p class=\u0026quot;isSelectedEnd\u0026quot;\u0026gt;Production serving infrastructure operates with clear SLAs, strong observability, and minimal downtime. Latency, availability, throughput, and GPU utilization are actively measured and optimized as platform demands grow.\u0026lt;/p\u0026gt;\n\u0026lt;h3\u0026gt;End-to-End Ownership\u0026lt;/h3\u0026gt;\n\u0026lt;p class=\u0026quot;isSelectedEnd\u0026quot;\u0026gt;You own the complete serving lifecycle — from deployment and release management through monitoring, optimization, and scaling — enabling ML engineers to ship quickly while maintaining reliability and reproducibility.\u0026lt;/p\u0026gt;\n\u0026lt;h3\u0026gt;Technical Leadership and Impact\u0026lt;/h3\u0026gt;\n\u0026lt;p\u0026gt;You shape the future of Wizard\u0026#39;s inference platform, driving key architectural decisions that improve performance, reduce infrastructure costs, and support the next generation of AI-powered shopping experiences.\u0026lt;/p\u0026gt;","departments":[{"id":4043640004,"name":"AI \u0026 Machine Learning","child_ids":[],"parent_id":4043638004}],"offices":[{"id":4026552004,"name":"Remote","location":"Remote","child_ids":[],"parent_id":null}]},{"absolute_url":"https://job-boards.greenhouse.io/wizardcommerce/jobs/5733929004","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":5018197004,"location":{"name":"Remote - USA"},"metadata":null,"id":5733929004,"updated_at":"2026-01-30T12:10:17-05:00","requisition_id":"99","title":"Senior Product Manager — Agentic AI Experiences","company_name":"Wizard","first_published":"2026-01-06T16:26:15-05:00","language":"en","application_deadline":null,"content":"\u0026lt;h3\u0026gt;\u0026lt;strong\u0026gt;About Wizard\u0026lt;/strong\u0026gt;\u0026lt;/h3\u0026gt;\n\u0026lt;p\u0026gt;Wizard is the top-performing AI Shopping Agent, delivering the best products from across the web with unmatched accuracy, quality, and trust.\u0026amp;nbsp;\u0026lt;/p\u0026gt;\n\u0026lt;h3\u0026gt;\u0026lt;strong\u0026gt;About the Role\u0026lt;/strong\u0026gt;\u0026lt;/h3\u0026gt;\n\u0026lt;p\u0026gt;Wizard is building an AI-powered shopping experience that feels natural, helpful, and human. This PM owns the product direction for our agentic experiences; how the Wizard agent understands intent, takes action, and guides users through conversations that convert.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;You’ll work closely with engineering, design, data, and our founder team to define how an AI agent behaves across channels, how it reasons about context, and how it supports end-to-end shopping flows. This work is zero-to-one, fast-moving, and central to Wizard’s strategy.\u0026lt;/p\u0026gt;\n\u0026lt;h3\u0026gt;\u0026lt;strong\u0026gt;What You’ll Do\u0026lt;/strong\u0026gt;\u0026lt;/h3\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Lead the roadmap for conversational and agentic AI features across mobile, web, and messaging surfaces.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Translate ambiguous user needs into structured AI behaviors, workflows, and product requirements.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Partner with engineering on inference pipelines, agent planning, retrieval, and orchestration logic.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Work with design to shape multimodal interactions, guided flows, and error-recovery patterns.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Establish metrics for engagement, task completion, and revenue impact; run experiments to improve them.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Build tight feedback loops with our early customers and retail partners.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Bring clarity and direction to a cross-functional team shipping quickly in a high-growth environment.\u0026lt;br\u0026gt;\u0026lt;br\u0026gt;\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;h3\u0026gt;\u0026lt;strong\u0026gt;What You Bring\u0026lt;/strong\u0026gt;\u0026lt;/h3\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;5–8+ years as a Product Manager in consumer tech or AI-driven products.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Experience with LLM-powered systems, agent frameworks, or conversational UX.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Ability to turn complex technical concepts into simple product decisions.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Strong instincts for user experience, especially within unstructured workflows.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Comfort operating in ambiguous, high-velocity settings.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Background working closely with engineering leads on systems architecture or ML applications is a plus.\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;h3\u0026gt;\u0026lt;strong\u0026gt;Compensation \u0026amp;amp; Benefits\u0026lt;/strong\u0026gt;\u0026lt;/h3\u0026gt;\n\u0026lt;p\u0026gt;The expected base salary range for this role is $185,000 – $235,000 USD, and will vary based on skills, experience, role level, and geographic location. Final compensation will be determined by considering these factors alongside overall role scope and responsibilities.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;In addition to base salary, Wizard offers:\u0026lt;/p\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Equity in the form of stock options\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Medical, dental, and vision coverage\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;401(k) plan\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Flexible PTO and company holidays\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Fully remote work within the United States\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Periodic company-wide offsites and team gatherings\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;p\u0026gt;Wizard is committed to fair, transparent, and competitive compensation practices.\u0026lt;/p\u0026gt;","departments":[{"id":4043639004,"name":"Product","child_ids":[],"parent_id":null}],"offices":[]},{"absolute_url":"https://job-boards.greenhouse.io/wizardcommerce/jobs/5867820004","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":5089694004,"location":{"name":"Remote - USA"},"metadata":null,"id":5867820004,"updated_at":"2026-04-08T10:51:35-04:00","requisition_id":"117","title":"Senior Python Engineer","company_name":"Wizard","first_published":"2026-04-08T10:51:35-04:00","language":"en","application_deadline":null,"content":"\u0026lt;h2\u0026gt;\u0026lt;strong\u0026gt;The Role\u0026lt;/strong\u0026gt;\u0026lt;/h2\u0026gt;\n\u0026lt;p\u0026gt;We’re looking for a Senior Software Engineer to build and scale the backend systems that power our AI agent. This role sits at the intersection of backend engineering, machine learning, and product, and is focused on turning AI capabilities into reliable, production-ready systems.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;You won’t be training models, but you will make them work in the real world. You’ll build APIs, services, and data systems that connect LLMs and ML models to user-facing experiences, ensuring performance, reliability, and scalability.\u0026lt;/p\u0026gt;\n\u0026lt;h2\u0026gt;\u0026lt;strong\u0026gt;What You’ll Do\u0026lt;/strong\u0026gt;\u0026lt;/h2\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Design and build APIs and backend services that power AI-driven product experiences\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Develop systems that integrate LLMs and ML models into production workflows\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Build and maintain data pipelines supporting training, inference, and evaluation\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Partner closely with ML Engineers, Data Scientists, and Product to ship end-to-end features\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Improve system performance, reliability, and scalability across services\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Contribute to experimentation and feedback loops that improve model and product performance\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Debug complex production issues and drive root cause resolution\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Raise the bar on code quality, system design, and engineering standards\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;h2\u0026gt;\u0026lt;strong\u0026gt;What Success Looks Like\u0026lt;/strong\u0026gt;\u0026lt;/h2\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;AI-powered features are reliably delivered through scalable, well-architected backend systems\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;ML and LLM capabilities are seamlessly integrated into product experiences with strong performance and uptime\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Clear, maintainable APIs and services enable fast iteration across engineering and product teams\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Systems are designed with strong observability, enabling rapid debugging and improvement\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Engineering decisions consistently balance speed, quality, and long-term scalability\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;h2\u0026gt;\u0026lt;strong\u0026gt;Ideal Background\u0026lt;/strong\u0026gt;\u0026lt;/h2\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;5+ years of experience in software engineering, with strong backend focus\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Strong proficiency in Python and experience building production-grade systems\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Experience designing APIs and service-oriented architectures\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Experience working with ML/AI systems in production environments (LLMs, ranking, recommendations, or similar)\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Familiarity with databases (SQL and/or NoSQL) and data-intensive systems\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Experience with cloud platforms (AWS, GCP, or Azure) and modern infrastructure\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Exposure to containerization and orchestration (Docker, Kubernetes)\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Ability to operate in ambiguity and take ownership of loosely defined problems\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Strong product mindset with focus on real user outcomes\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Clear communication and ability to collaborate across engineering, ML, and product\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;h2\u0026gt;\u0026lt;strong\u0026gt;Compensation \u0026amp;amp; Benefits\u0026lt;/strong\u0026gt;\u0026lt;/h2\u0026gt;\n\u0026lt;p\u0026gt;The expected base salary range for this role is $200,000–$225,000 USD and will vary based on skills, experience, role level, and geographic location. Final compensation will be determined by considering these factors alongside overall role scope and responsibilities.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;In addition to base salary, Wizard offers:\u0026lt;/p\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Equity in the form of stock options\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Medical, dental, and vision coverage\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;401(k) plan\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Flexible PTO and company holidays\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Fully remote work within the United States\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Periodic company offsites and team gatherings\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;p\u0026gt;Wizard is committed to fair, transparent, and competitive compensation practices.\u0026lt;/p\u0026gt;","departments":[{"id":4043640004,"name":"AI \u0026 Machine Learning","child_ids":[],"parent_id":4043638004}],"offices":[{"id":4026552004,"name":"Remote","location":"Remote","child_ids":[],"parent_id":null}]}],"meta":{"total":6}}