Chai DiscoveryResearch Engineer - ML Infrastructure
AI in this role
Build and optimize core ML training and infrastructure frameworks for frontier biochemical AI models at Chai Discovery.
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.
Make our ML training stack maximally reliable: 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.
How we rate this
Research Engineer - ML Infrastructure at Chai Discovery rates 95 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- Tell me about a research question you investigated. What did you find?
- Tell me about a project where ml infrastructure was part of your work. What did you do?
- Tell me about a project where distributed training was part of your work. What did you do?
- Tell me about a project where optimization was part of your work. What did you do?
- Tell me about a project where fault tolerance was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: AI Research, ML Infrastructure, Distributed Training, Optimization, and Fault Tolerance. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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