Machine Learning Engineer - Biological Foundation Models

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/Tensor Flow 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.