Build the AI platform that empowers scientists to direct research programs with agents at Chai Discovery.
Posted by employer 21 hours ago
First seen on Joblaze 15 hours ago
Last verified on the company career page 15 hours ago
Skills & Technologies
What you'll build
Must have
AI in the day-to-day
Chai trains frontier models that learn biochemical structures and interactions to assist scientists.
Not disclosed in this posting: compensation, years of experience, visa sponsorship.
Joblaze summary
In this role, the Software Engineer will develop an AI platform that enables scientists to manage research programs through advanced agents. Key skills include strong software architecture and experience with complex applications, particularly in agent systems. This position is ideal for someone with a solid background in software development who can navigate and simplify intricate domains. Chai Discovery emphasizes a culture of high velocity and ownership, positioning itself at the forefront of AI in life sciences.
Joblaze insights
Quick facts
From the original posting
Build the AI platform that lets scientists direct research programs with agents. As agents become more capable, they will increasingly use Chai’s foundation models to design molecules and guide experiments. We believe scientists should be able to work at the level of the questions they want answered and delegate more of the work required to answer them; you will build the platform that makes this possible.
Work with scientists and researchers to define how people, agents, and models work together throughout a therapeutic research program.
Build products that shorten the cycle from an idea to an experiment to the next decision, while keeping scientists able to understand and direct the work.
Build the core abstractions for agent execution, model inference, and scientific tools so new capabilities can work together across the workspace.
We look for strong software architecture and product judgment, with depth in complex applications or agent systems. You can find clear abstractions in a messy domain and build systems that hold up as their scope grows. We value a practical understanding of where agents fail and how to tell whether they’re doing useful work.
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