Provide technical leadership in biomedical foundation models and clinical translation
Shape application of genomic, pathology, and multimodal foundation models
Collaborate with AI scientists, translational scientists, and clinical partners
Lead application and post-training of foundation models for biomarker discovery
Design workflows that extract biologically interpretable signals
Ensure modeling outputs align with biological plausibility and clinical relevance
Build and evaluate advanced ML models across various data types
Collaborate with translational science and clinical teams

Requirements

PhD in Computational Biology, Bioinformatics, Computer Science, or a related quantitative field
5+ years of experience applying ML to biological, genomic, or clinical data in oncology, immunology, or translational medicine
Deep experience with foundation models, representation learning, self-supervised learning, or deep sequence models
Demonstrated ability to translate ML outputs into biological insight or clinical value
Strong proficiency in PyTorch and modern ML tooling (e.g., HuggingFace transformers, PEFT, Captum, MLFow)
Track record of scientific and technical leadership through project ownership, mentorship, or cross-team influence

Benefits

Comprehensive medical, dental, vision, life, and disability plans
Free testing for employees and their immediate families
Fertility care benefits
Pregnancy and baby bonding leave
401k benefits
Commuter benefits
Employee referral program

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningfoundation modelsrepresentation learningself-supervised learningdeep sequence modelsbiomarker discoveryworkflow designbiologically interpretable signalsdata evaluationgenomic data analysis

Soft Skills

technical leadershipcollaborationmentorshipproject ownershipcross-team influence

Certifications

PhD in Computational BiologyPhD in BioinformaticsPhD in Computer Science