Machine Learning Engineer – Biological Foundation ModelsMetric Bio has partnered with a venture-backed biotech at the intersection of AI and cell biology. This team is building foundation models on trillion-token scale biological datasets to reimagine how we create cell therapies.
This is a role for someone who doesn’t just apply existing methods but creates new ones; first-author researchers, system builders, and innovators who want their work to drive real therapeutic impact.
Responsibilities:
Design and optimize foundation modelsfor single-cell and multi-omics data, leveraging transformer and generative architectures.
Build scalable distributed pipelines(multi-GPU training, trillion-token inference) to push biology into true foundation-scale.
Collaborate closely with computational biologists and wet-lab teams, ensuring models produce interpretable, biologically meaningful outputs.
Prototype and deploy novel architecturestailored to biological data, with the freedom to shape strategy and direction.
Requirements:
First-author publicationsin top-tier ML/biology journals.
6+ years of experience in ML, deep learning, or foundation models (academic or industry).
Proven expertise withtransformers, diffusion, or generative models.
Strong Python + PyTorch/TensorFlow engineering skills; ability to move from research prototype → production.
Background in single-cell or omics data is ideal, butML-first innovators who can quickly learn the biology are very welcome.
Track record of innovation: new methods, impactful papers, or deployed ML systems.
What We Offer:
Technical leadership opportunity at a mission-driven company that has recently securedover $50M in funding.
Work alongside top talent at the cutting edge ofAI x biology.
Chance to impact millions of lives byredefining how cell therapies are developed.
Competitive compensation and benefits, with an emphasis on urgency, collaboration, and innovation.
