Machine Learning Engineer, Life Sciences

Core responsibilities:

• Lead scientific research with partners to interpret advanced biological foundation models (genomic foundation models, ViTs, PLMs) to uncover what they've learned
• Own research delivery on high-stakes projects with customers and do whatever it takes to make delivery successful, including problem and hypothesis definition, data sourcing, tool building, iteration, and implementation
• Productionize interpretability research into maintainable tools, APIs, and workflows that work on real models and real scientific data
• Optimize pipelines and infrastructure for frontier model interpretability, training, and inference

This role contributes to developing groundbreaking technology on the critical path to ensuring a safe and beneficial future for humanity by advancing interpretability of AI systems, particularly in life sciences applications like precision medicine, digital pathology, and drug discovery.

Required skills and experience:

• 5+ years of experience in ML infra, research engineering, or systems programming
• Comfort working across research and engineering boundaries
• Expertise in Python, PyTorch or Jax, and distributed systems
• Experience deploying and maintaining ML systems at scale
• Experience with biological/life sciences ML (computational biology, bioinformatics, digital pathology, protein/genomics, multimodal biomedical data) preferred

Market competitive salary, equity, and competitive benefits. The expected salary range for this position is $200,000 - $400,000 USD (depending on level and scope).