{"jobs":[{"absolute_url":"https://job-boards.greenhouse.io/superluminalrx/jobs/5227202008","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":4478124008,"location":{"name":"Boston, MA"},"metadata":null,"id":5227202008,"updated_at":"2026-05-21T11:44:01-04:00","requisition_id":"68","title":"Scientist, Machine Learning (Principal Scientist - Associate Director)","company_name":"Superluminal Medicines, Inc.","first_published":"2026-05-21T11:44:00-04:00","language":"en","application_deadline":null,"content":"\u0026lt;div class=\u0026quot;content-intro\u0026quot;\u0026gt;\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;About Superluminal Medicines:\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;Superluminal Medicines is a generative biology and chemistry company revolutionizing the speed and accuracy of how small molecule medicines are created. The Company’s platform aims to create candidate-ready compounds with unprecedented speed using a combination of deep biology, computational and medicinal chemistry, machine learning, and proprietary big data infrastructure. We are expanding the team of talented scientists who seek to build the future of small molecule drug discovery with creativity and innovation.\u0026lt;/p\u0026gt;\u0026lt;/div\u0026gt;\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;About the Role:\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;We are seeking a Machine Learning Scientist to join our integrated discovery team and help advance small molecule drug discovery programs through applied ML. In this role, leading from the bench, you will enable the development, validation and deployment of state-of-the-art ML models to generate the quantitative predictions necessary to drive drug discovery. Beyond technical mastery, you will serve as a core strategic partner to medicinal chemists, computational chemists, and biologists, building models that move programs efficiently toward program decision points and candidate nomination.\u0026amp;nbsp;\u0026amp;nbsp;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Key Responsibilities:\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Lead the application of Large Language Models (LLMs), co-folding algorithms, and generative chemistry techniques to design novel chemical matter aimed at hitting key program milestones, such as establishing selectivity windows and optimizing drug-like properties\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Serve as the machine learning POC on cross functional projects partnering\u0026amp;nbsp; with medicinal chemists and structural biologists to refine SAR and structure informed modeling efforts\u0026amp;nbsp;\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Synthesize complex ML outputs into clear, actionable design hypotheses that cross-functional scientific stakeholders can use to make high-stakes program decisions\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;May be responsible for management and development of internal team members\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Required Qualifications:\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Ph.D. in Computational Chemistry, Computer Science, Machine Learning, or a related field\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;2+ years applying ML methods in a small molecule drug discovery programs in biotech or pharma environments\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Demonstrated expertise in statistics, probability theory, data modeling, machine learning algorithms, and the languages used to implement analytics solutions\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Demonstrated success in a cross-functional environment, including biologists, structural biologists, medicinal and computational chemists, with specific examples of computational designs/algorithms/models that directly influence achievement of program milestones\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Strong practical proficiency in Python and deep learning libraries (e.g., PyTorch, TensorFlow) is required. Demonstrated ability to build and maintain robust, production-quality ML code and data workflows\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Preferred Qualifications:\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Proven experience with protein-ligand co-folding models (e.g.,Boltz, OpenFold, AlphaFold, etc) and the ability to integrate these structural insights into broader ML discovery pipelines\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Expertise fine-tuning existing models with internally generated structural biology and biology data\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Strong knowledge of deep learning frameworks, specifically for affinity prediction, ADMET modeling, and the application of LLMs in a biological or chemical context\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Experience mentoring and developing teams\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Skills \u0026amp;amp; Competencies:\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;A demonstrated track record of innovation in the ML/AI space, including developing and validating new architectures or novel applications of existing models to solve complex drug discovery problems\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Demonstrated expertise using small molecule drug discovery ML/AI tools e.g. AlphaFold, Boltz, OpenFold, ChemProp, DeepChem, Reinvent, etc)\u0026amp;nbsp;\u0026amp;nbsp;\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Strong level coding for ML tasks including knowledge of key packages (RDKit, scikit-learn, numpy, pandas, pytorch, DeepChem, polars, PyG/DGL).\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Strong interpersonal and communications skills in the \u0026quot;why\u0026quot; behind a design to a diverse scientific audience\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\u0026lt;div class=\u0026quot;content-conclusion\u0026quot;\u0026gt;\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Benefits:\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;Superluminal offers a comprehensive benefits package that fully covers employees’ annual deductibles and monthly premiums for medical, dental, and vision insurance. The package also includes a 401(k) match program, a Massachusetts transportation subsidy, equity, unlimited paid time off, and both disability and life insurance.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Equal Opportunity Statement:\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;Superluminal Medicines is an Equal Opportunity Employer committed to a culturally diverse workforce. All qualified applicants will receive consideration for employment without regard to race; color; creed; religion; national origin; age; ancestry; nationality; marital, domestic partnership or civil union status; sex, gender, gender identity or expression; affectional or sexual orientation; disability; veteran or military status or liability for military status.\u0026lt;/p\u0026gt;\u0026lt;/div\u0026gt;","departments":[{"id":4016720008,"name":"Machine Learning","child_ids":[],"parent_id":null}],"offices":[{"id":4013179008,"name":"Boston, MA","location":"Boston, MA","child_ids":[],"parent_id":null}]},{"absolute_url":"https://job-boards.greenhouse.io/superluminalrx/jobs/5234812008","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":4481762008,"location":{"name":"Boston, MA"},"metadata":null,"id":5234812008,"updated_at":"2026-06-01T16:15:01-04:00","requisition_id":"71","title":"Scientist-Principal Scientist, Protein Design \u0026 Structure","company_name":"Superluminal Medicines, Inc.","first_published":"2026-06-01T16:15:01-04:00","language":"en","application_deadline":null,"content":"\u0026lt;div class=\u0026quot;content-intro\u0026quot;\u0026gt;\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;About Superluminal Medicines:\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;Superluminal Medicines is a generative biology and chemistry company revolutionizing the speed and accuracy of how small molecule medicines are created. The Company’s platform aims to create candidate-ready compounds with unprecedented speed using a combination of deep biology, computational and medicinal chemistry, machine learning, and proprietary big data infrastructure. We are expanding the team of talented scientists who seek to build the future of small molecule drug discovery with creativity and innovation.\u0026lt;/p\u0026gt;\u0026lt;/div\u0026gt;\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Position Summary\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;We are seeking a highly innovative and experienced\u0026amp;nbsp;\u0026lt;strong\u0026gt;Scientist/Senior Scientist/Principal Scientist\u0026lt;/strong\u0026gt; to lead GPCR protein engineering and production efforts. The successful candidate will be a subject matter expert (SME) in membrane protein biochemistry, responsible for developing and implementing strategies to express, purify, and characterize challenging G-protein coupled receptors (GPCRs) to support structural biology, biophysics, and drug discovery programs. This role involves hands-on laboratory work and scientific leadership.\u0026amp;nbsp;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Key Responsibilities\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;\u0026lt;strong\u0026gt;Protein Engineering Strategy:\u0026lt;/strong\u0026gt;\u0026amp;nbsp;Design, execute, and optimize constructs for GPCR expression.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;\u0026lt;strong\u0026gt;Expression \u0026amp;amp; Purification:\u0026lt;/strong\u0026gt;\u0026amp;nbsp;Drive the development of high-throughput protein expression (mammalian/insect cells) and purification workflows, ensuring high-quality, homogeneous protein for assays and structural studies (Cryo-EM).\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;\u0026lt;strong\u0026gt;Cross-Functional Collaboration:\u0026lt;/strong\u0026gt;\u0026amp;nbsp;Partner with pharmacology, structural biology, and medicinal chemistry teams to support drug discovery projects and advance programs from hit-to-lead and lead optimization.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;\u0026lt;strong\u0026gt;Technical Leadership:\u0026lt;/strong\u0026gt;\u0026amp;nbsp;Serve as a SME on GPCR protein sciences in internal and external project teams; evaluate and implement new technologies.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;\u0026lt;strong\u0026gt;Scientific Documentation \u0026amp;amp; Communication:\u0026lt;/strong\u0026gt;\u0026amp;nbsp;Document experiments in ELN, present data at internal/external meetings, and publish research findings.\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Required Qualifications\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Ph.D. in Biochemistry, Molecular Biology, Biophysics, or a related field with 2+ years (Scientist), 5+ years (Senior Scientist), or 8+ years (Principal Scientist) of relevant experience;\u0026lt;strong\u0026gt; or\u0026lt;/strong\u0026gt; a master’s or bachelor’s degree in a related field with commensurate industry experience.\u0026amp;nbsp;\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Deep knowledge of GPCR biology and membrane protein structure-function relationships.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Proven expertise in molecular biology (cloning, construct design, mutagenesis).\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Hands-on experience in protein purification of GPCRs using FPLC (affinity, SEC).\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Familiarity with protein stabilization techniques (e.g., antibody fragments, fusion partners, mutagenesis).\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Excellent communication, interpersonal, and leadership skills.\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Preferred Qualifications\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Experience with structural biology methods (data collection and processing for Cryo-EM).\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Experience in laboratory automation in protein sciences \u0026amp;amp; purification.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Background in computational protein design tools (e.g., Rosetta, AI/ML-driven prediction).\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\u0026lt;div class=\u0026quot;content-conclusion\u0026quot;\u0026gt;\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Benefits:\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;Superluminal offers a comprehensive benefits package that fully covers employees’ annual deductibles and monthly premiums for medical, dental, and vision insurance. The package also includes a 401(k) match program, a Massachusetts transportation subsidy, equity, unlimited paid time off, and both disability and life insurance.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Equal Opportunity Statement:\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;Superluminal Medicines is an Equal Opportunity Employer committed to a culturally diverse workforce. All qualified applicants will receive consideration for employment without regard to race; color; creed; religion; national origin; age; ancestry; nationality; marital, domestic partnership or civil union status; sex, gender, gender identity or expression; affectional or sexual orientation; disability; veteran or military status or liability for military status.\u0026lt;/p\u0026gt;\u0026lt;/div\u0026gt;","departments":[{"id":4083972008,"name":"Protein Design \u0026 Structure","child_ids":[],"parent_id":null}],"offices":[{"id":4013179008,"name":"Boston, MA","location":"Boston, MA","child_ids":[],"parent_id":null}]},{"absolute_url":"https://job-boards.greenhouse.io/superluminalrx/jobs/5225651008","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":4477329008,"location":{"name":"Boston, MA"},"metadata":null,"id":5225651008,"updated_at":"2026-05-20T15:00:29-04:00","requisition_id":"67","title":"Senior Scientist - Principal Scientist, Computational Chemistry","company_name":"Superluminal Medicines, Inc.","first_published":"2026-05-20T15:00:29-04:00","language":"en","application_deadline":null,"content":"\u0026lt;div class=\u0026quot;content-intro\u0026quot;\u0026gt;\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;About Superluminal Medicines:\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;Superluminal Medicines is a generative biology and chemistry company revolutionizing the speed and accuracy of how small molecule medicines are created. The Company’s platform aims to create candidate-ready compounds with unprecedented speed using a combination of deep biology, computational and medicinal chemistry, machine learning, and proprietary big data infrastructure. We are expanding the team of talented scientists who seek to build the future of small molecule drug discovery with creativity and innovation.\u0026lt;/p\u0026gt;\u0026lt;/div\u0026gt;\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;About the Role:\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;We are seeking a high-impact Computational Chemist to join our integrated discovery team. In this role, you will be the computational engine of our programs, combining physics-based modeling, machine learning and structural biology to generate the quantitative predictions and develop necessary workflows to drive small molecule drug discovery. You will serve as a core strategic partner to medicinal chemists and biologists, focusing on compound design and tool development to impact discovery pipeline and address unmet computational needs.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Key Responsibilities:\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Integrate physics-based simulations with ML predictions to achieve the quantitative accuracy required to prioritize compounds for synthesis\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Collaborate with a team of interdisciplinary scientists to develop actionable hypotheses and design computational experiments\u0026amp;nbsp;\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Design and prioritize chemical matter specifically aimed at hitting key program milestones, such as establishing in vivo POC, achieving selectivity windows, or optimizing ADMET profiles for candidate selection\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Develop, validate and deploy computational workflows to optimize the\u0026amp;nbsp; \u0026quot;Design-Make-Test-Analyze\u0026quot; cycles and address gaps\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Required Qualifications:\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Ph.D. in Computational Chemistry, Biophysics, or a related field\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;1-3+ years of experience in a biotech or pharma setting performing computational support for small molecule drug discovery\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Advanced knowledge of physics-based and ML computational chemistry packages including knowing when and how to deploy various tools for maximum project impact\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Exceptional ability to communicate the \u0026quot;why\u0026quot; behind a design to a diverse scientific audience\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Design experience working in concert with medicinal chemistry teams to design synthesizable compounds that efficiently work towards defined goals of activity, affinity, selectivity, properties, etc\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;A proven track record for innovation in structure-based small molecule drug discovery including developing and validating new workflows and techniques or expansions of existing ones\u0026amp;nbsp;\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Preferred Qualifications:\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Experience working with structural biology teams to extract the most information possible from cryo-EM and x-ray crystallography experiments and using this to accelerate programs using structure-based drug discovery techniques\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Proven experience using ML to scale physics-based insights, specifically in the context of large-scale virtual screening or FEP-guided lead optimization\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;A proven track record for innovation in structure-based small molecule drug discovery including developing and validating new workflows and techniques or expansions of existing ones\u0026amp;nbsp;\u0026amp;nbsp;\u0026amp;nbsp;\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Skills \u0026amp;amp; Competencies:\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Expert level use of structure-based small molecule drug discovery software tools including protein preparation, docking, FEP, QM, conformer selection. (Schrodinger suite, OpenEye, MOE, etc)\u0026amp;nbsp;\u0026amp;nbsp;\u0026amp;nbsp;\u0026amp;nbsp;\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Ability to work directly in a Linux-based environment\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Familiarity with cloud computing infrastructure (AWS, GCS) is a plus\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Python scripting and prototyping experience including knowledge of key packages (RDKit, scikit-learn, numpy, pandas, pytorch, etc)\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\u0026lt;div class=\u0026quot;content-conclusion\u0026quot;\u0026gt;\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Benefits:\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;Superluminal offers a comprehensive benefits package that fully covers employees’ annual deductibles and monthly premiums for medical, dental, and vision insurance. The package also includes a 401(k) match program, a Massachusetts transportation subsidy, equity, unlimited paid time off, and both disability and life insurance.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Equal Opportunity Statement:\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;Superluminal Medicines is an Equal Opportunity Employer committed to a culturally diverse workforce. All qualified applicants will receive consideration for employment without regard to race; color; creed; religion; national origin; age; ancestry; nationality; marital, domestic partnership or civil union status; sex, gender, gender identity or expression; affectional or sexual orientation; disability; veteran or military status or liability for military status.\u0026lt;/p\u0026gt;\u0026lt;/div\u0026gt;","departments":[{"id":4016719008,"name":"Computational Chemistry","child_ids":[],"parent_id":null}],"offices":[{"id":4013179008,"name":"Boston, MA","location":"Boston, MA","child_ids":[],"parent_id":null}]}],"meta":{"total":3}}