Research Engineer - ML Infrastructure

Chaidiscovery

San Francisco office, US

Who this role is best for

Best suited to candidates with strong software system design skills and experience in AI/ML infrastructure working in life sciences and large-scale GPU environments.

Best fit for

Candidates with experience optimizing AI/ML workloads for large GPU clusters and deep technical expertise in model training frameworks
— “Profile end-to-end training runs on large GPU clusters”
Professionals with fluency in Python and ML frameworks like PyTorch or JAX and a background in AI/ML infrastructure
— “proficiency in Python, and Pytorch or JAX fluency”

Things to consider

The team is focused on pushing AI frontiers in drug discovery, which may demand long-term commitment to cutting-edge research
— “This is a chance to push the frontier of AI drug design”

How to stand out

Highlight experience with model training frameworks and optimization techniques like quantization and parallelism
— “Build our training stack across model, layer, and kernel levels; optimize workloads through parallelism, quantization, and custom kernels”
Emphasize your ability to ensure reliability in large-scale ML systems, including fault tolerance and checkpointing
— “Own reliability of the training stack: fault tolerance, checkpointing, and deterministic orchestration for long-running, large-scale jobs”
Showcase your work on real-world AI systems with measurable impact, especially in scientific or industrial applications
— “technical spikes where you have gone deep and demonstrated exceptional impact on real-world problems and systems”
Demonstrate your ability to work in close collaboration with researchers and engineers on complex technical problems
— “in close partnerships with fellow researchers and engineers”

Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

optimized workloads through parallelism
eliminated bottlenecks and failures
monitored throughput, utilization, and uptime

Typical background

AI/ML teamssoftware system design

Skills & requirements

Required

ML InfrastructureModel TrainingEvaluationOptimizationParallelismQuantizationCustom KernelsFault ToleranceCheckpointingDeterministic Orchestration

Preferred

AI Drug DesignBiological Systems

Stack & domain

PythonPyTorchJAXAIMLDrug Discovery

About the role

Original posting from Chaidiscovery via Ashby

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 https://endpoints.news/eli-lilly-chai-discovery-sign-ai-software-deal/, Pfizer https://www.forbes.com/sites/amyfeldman/2026/06/04/why-pfizer-and-eli-lilly-are-betting-on-this-13-billion-ai-drug-discovery-startup/, and Novartis https://www.chaidiscovery.com/news/novartis-partnership 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.

Source: Chaidiscovery careers (Ashby)