Join Chai Discovery as a Software Engineer to optimize AI models for drug discovery in a fast-paced, innovative environment.
Posted by employer 9 months ago
First seen on Joblaze 1 week ago
Last verified on the company career page 1 day ago
Skills & Technologies
AI in the day-to-day
Chai builds models that learn biochemical structures to accelerate drug discovery.
Requirements
Not disclosed in this posting: compensation, work arrangement, visa sponsorship.
Joblaze summary
In this role, the software engineer focuses on optimizing and maintaining the infrastructure that supports Chai Discovery's advanced AI models, ensuring they operate efficiently and reliably at scale. Key skills include experience with performance tuning, distributed systems, and machine learning serving, particularly in areas like GPU utilization and batching. This position is ideal for seasoned engineers with a strong track record in production systems who thrive on solving complex challenges. Chai Discovery fosters a culture of ownership and rapid innovation, making it a dynamic environment for technical excellence.
Joblaze insights
Quick facts
From the original posting
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
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.
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.