Flagship Pioneering, Inc.
Machine Learning Scientist
Somerville, MA USA$132k–$209kmidAdded today
About this role
Join a clinical-stage biotech company to develop and fine-tune foundation models for drug discovery using single-cell omics and perturbation data. You'll build AI methods to accelerate target identification and early-stage therapeutic development while collaborating with interdisciplinary biology and chemistry teams.
What you'll do
- Apply and fine-tune foundation models (transformers, diffusion, VAEs) on single-cell RNA-seq and multi-modal perturbation data to model disease states
- Build perturbation models using CRISPR, small molecule, and cytokine screening data with emphasis on mechanistic interpretability
- Develop gene network inference and causal representation methods to support target identification
- Deploy generative AI models on cloud platforms (AWS/GCP) and establish benchmarking frameworks for clinical relevance
- Collaborate with biology, chemistry, and omics teams to translate research questions into ML solutions
- Stay current with latest advances in foundation models, representation learning, and perturbation modeling
What they're looking for
- Deep learning and generative AI (transformers, diffusion models, VAEs)
- Single-cell RNA-seq and high-dimensional biological data analysis
- CRISPR perturbation screening and Perturb-seq data modeling
- Single-cell foundation models (Geneformer, scGPT, scFoundation)
- Cloud computing and MLOps (AWS/GCP)
- Statistical inference and causal representation learning
- Gene regulatory networks and multi-omics integration
- Scientific communication and interdisciplinary collaboration
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Flagship Pioneering, Inc.
Flagship Pioneering builds unmanned aerial systems (UAS) and hardware for advanced aerial applications. The company is hiring hardware engineers and UAS specialists to design, develop, and operate fixed-wing UAV systems for demanding missions.
- Website
- flagshippioneering.com
Likely interview questions
- Describe your experience fine-tuning or pre-training foundation models on biological data—what were the key challenges and how did you address them?
- How have you approached interpretability in deep learning models for biological discovery, and what methods have been most effective for translating model outputs to biological insights?