Machine Learning Researcher - Genomics Foundation Models - PR/571643_1768175254

Machine Learning Researcher - Genomics Foundation Models

Cambridge

USD200000 - USD300000

PR/571643_1768175254

Machine Learning Researcher - Predictive Cell-State Modeling

We're partnered with a start-up building a foundation model for cell‑state transitions. This means generating rich, context‑aware datasets spanning diverse cellular states and sequential perturbations (timing, dose, and order) to learn the causal logic of signaling. Their approach couples massively parallel wet‑lab experimentation with modern ML (transformers, diffusion/continuous‑time models) to move biology from trial‑and‑error toward in silico protocol design for cell manufacturing, disease modeling, and therapeutic discovery. Backed by leading biotech and tech venture capitalist firms and sheparded by leading minds in biology and AI. It's a rare opportunity to be a driving force for real impact.

The Role

You'll develop ML systems that learn the grammar of signaling and forecast cell‑state transitions under multi‑step programs (order, dose, timing). You will:

Build sequence/dynamics models (e.g., transformers with schedule awareness; simple continuous‑time layers) to predict trajectories.
Co‑embed internal single‑cell data with public references to generalize across diverse starting contexts.
Run a light active‑learning loop to prioritize the next experimental batches.
Translate predictions into actionable signal schedules (protocols) with the wet‑lab team.

What You'll Bring

PhD or equivalent in ML, Computational Biology, Bioinformatics, or similar.
Hands‑on experience with developing novel generative architectures sequence models or trajectory/dynamics modeling; comfort with single‑cell/multi‑omic data.
Proficiency in Python and PyTorch/JAX; familiarity with distributed training and reproducible pipelines.
Ability to collaborate closely with experimental scientists and deliver usable tools, not just papers.

Why This Matters

Predicting how cells respond to signals unlocks rational protocol design and context‑specific response prediction to compress months of lab iteration into days of computation and accelerating routes to real therapies.

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