Software Engineer, Platform & Inference

About Chai Discovery

Chai is a research lab working on generative models for molecular design. We are building AI infrastructure to engineer new medicines with speed and precision. The world’s largest pharmaceutical companies—including 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—have started adopting our platform across their organizations.

Our mission is to unlock progress towards better cures and better science, and we see countless interesting problems on the road ahead. We are known for talent density, rigorous research and pace of execution.

Our founders have been at the forefront of this field from the beginning. We are backed by Sequoia, Index, Thrive, General Catalyst, Dimension, OpenAI and others.

About the role

Platform engineers make Chai's models fast, cheap, and reliable at scale, and enable the outer loop that accelerates research: the infrastructure and software abstractions used to train, eval, and understand models.

You'll own the serving stack that turns our frontier models into a product scientists depend on: latency, throughput, GPU efficiency, batching, and autoscaling across a large multi-cloud GPU fleet. You'll also contribute to the work that enables turning raw models into product-ready pipelines, and the experiment and observability tooling that lets a researcher ship faster.

You've built high-performance services that developers love, moved ML systems into production at scale, and can see around corners before they become outages.

You'll work closely with the researchers who train the models, the product engineers who build on them, and the commercial team deploying them to the world's largest pharma companies.

About you

We index on systems judgment, ownership, and the scars that come from having run production infrastructure before. We're looking for engineers who get obsessed with hard problems and don't give up easily. We look for:

• 4+ years building production systems, with real depth in performance, distributed systems, or ML serving

• Experience optimizing model inference: GPU utilization, batching, quantization, caching, or kernel-level work

• A platform mindset: you like building the tools and abstractions that make other engineers and researchers faster

• End-to-end ownership of 24/7 systems, including observability, alerting, and incident response

• Experience across both 0-to-1 buildouts and 1-to-n scale-ups, with an always-evolving playbook you bring wherever you go

• The instinct to treat cost and efficiency as first-class constraints, not afterthoughts

A background in biology is not required. What makes the difference is technical excellence, curiosity about the domain, and grit.

WE OFFER

The opportunity to work at the leading edge of AI research, with world-class people, on a mission that matters. We protect & promote a culture of high velocity and ownership. We offer highly competitive compensation.