Research Engineer - ML Infrastructure

Reposted An Hour Ago
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San Francisco, CA, USA
In-Office
Mid level
Artificial Intelligence • Biotech
The Role
Build and optimize distributed machine learning infrastructure for large-scale model training and inference. Responsibilities include profiling training runs, improving compute, communication, and storage efficiency, developing parallelism and quantization strategies, creating CUDA and Triton kernels, and ensuring fault tolerance, checkpointing, deterministic orchestration, and reliability across GPU clusters. The role partners closely with research scientists to scale new model architectures and training recipes.
Summary Generated by Built In
About Chai Discovery

Chai builds the design suite for molecules. We train frontier models that learn the underlying foundations of biochemical structure and interaction, so scientists can move faster and pursue targets that other methods cannot reach.

AI is reinventing life sciences the same way it reinvented software engineering, and Chai is at the forefront of this shift. Leading pharmaceutical companies like Eli Lilly, Pfizer, and Novartis are adopting our platform to power their drug discovery programs.

We value diverse perspectives and are ready to find greatness in unexpected places.

About the role

Make our models performant, resource efficient and reliable at scale by developing the core frameworks for model training and evaluation, in close partnerships with fellow researchers and engineers.

  • Build our training stack across model, layer, and kernel levels; optimize workloads through parallelism, quantization, and custom kernels.

  • Profile end-to-end training runs on large GPU clusters; eliminate bottlenecks and failures; monitor throughput, utilization, and uptime.

  • Ensure new model architectures and training recipes scale efficiently, from early experiments to frontier-scale runs.

  • Own reliability of the training stack: fault tolerance, checkpointing, and deterministic orchestration for long-running, large-scale jobs.

Chai's models are moving beyond protein structure prediction into real-world therapeutic engineering. This is a chance to push the frontier of AI drug design, working alongside a rigorous and craft-obsessed team.

About you

Ideal backgrounds include deep industry experience working with top AI/ML teams on the kinds of problems and systems we describe above—with strong software system design skills, proficiency in Python, and Pytorch or JAX fluency. We look for technical spikes where you have gone deep and demonstrated exceptional impact on real-world problems and systems.

We offer

The opportunity to work at the vanguard of AI research and frontier biology, with world-class people, on a mission that matters. We protect & promote a culture of high velocity and ownership. We compensate our team accordingly.

Skills Required

  • 4+ years of industry experience working within AI or ML infrastructure teams
  • Proficiency in Python and PyTorch or JAX
  • Strong software systems design skills across model code, systems, and kernels
  • Experience orchestrating GPU clusters and large-scale model training
  • Experience optimizing ML workloads using parallelism, quantization, and CUDA or Triton kernels
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The Company
HQ: San Francisco, California
9 Employees

What We Do

Building frontier artificial intelligence to predict and reprogram the interactions between biochemical molecules.

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