Senior / Principal ML Scientist, Foundation Models for Life Sciences

What you'll do

• Design and optimize distributed training pipelines and GPU-cluster workloads for structure prediction, co-folding, and generative models.
• Own production ML systems end to end, including deployment, serving, monitoring, reliability, and integration with experimental feedback loops.
• Set ML engineering direction and standards, translating research prototypes into scalable systems with scientists and researchers.

What they ask for

• Extensive hands-on experience building and operating production ML systems at scale.
• Deep expertise in distributed training infrastructure, including large-scale GPU clusters.
• Strong software engineering fundamentals, including system design, production-grade code, CI/CD, observability, and reliability.
• Proficiency in PyTorch, JAX, or TensorFlow, with experience optimizing training and inference performance.

Nice to have

• Experience with generative-model infrastructure for biological sequences, molecular structures, or scientific data; structure prediction or co-folding experience is a plus.
• Experience with agentic frameworks, active learning or closed-loop experiments, open-source ML infrastructure, life sciences, or scientific model evaluation.

Pay

$268,000 — $384,000 USD (base salary)

Tools

AWS, GCP, PyTorch, JAX, TensorFlow, AlphaFold, Boltz, Protenix

AI focus

Build infrastructure for foundation models involving biological structure prediction and co-folding, sequence design, generative modeling, multimodal scientific reasoning, and closed-loop experimental discovery.