Machine Learning Engineer - Biological Foundation Models

Metric 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 models for 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 architectures tailored to biological data, with the freedom to shape strategy and direction.

Requirements:

• First-author publications in top-tier ML/biology journals.
• 6+ years of experience in ML, deep learning, or foundation models (academic or industry).
• Proven expertise with transformers, 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, but ML-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 secured over $50M in funding.
• Work alongside top talent at the cutting edge of AI x biology.
• Chance to impact millions of lives by redefining how cell therapies are developed.
• Competitive compensation and benefits, with an emphasis on urgency, collaboration, and innovation.