Warp is the AI-driven U.S. chemistry CRO that turns AI-designed molecules into compounds drug discovery teams can test, in labs built for automation. Backed by Menlo Ventures.
About the role
Own the post-training loop from identifying a capability gap through evaluation, data development, training, deployment, and production feedback. Build evals grounded in product needs, with clear baselines and held-out tests that distinguish real progress from benchmark overfitting. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Experience working at the frontier of model capabilities, with meaningful personal ownership of post-training results
- A demonstrated ability to achieve state-of-the-art results and substantiate them with rigorous evaluation against strong baselines
- Deep practical experience with reinforcement learning, including reward design, rollout systems, training stability, and diagnosing failure modes
- An eval-led approach: you define what success means before optimizing and scrutinize whether measured gains reflect the intended behavior
- Strong data intuition, backed by experience identifying, creating, and selecting data that produces measurable improvements
- Strong engineering skills and experience building reliable training and experimentation systems
More about this role
At Warp Sciences, our mission is to help the world discover new medicines faster—and build the capacity to do it here in America. We bring AI, robotics, and life sciences together in automated labs that design, run, and learn from experiments, with each experiment making the next one better. We’re not developing drugs of our own. We’re building the infrastructure for the people who do.
Warp is looking for a post-training research engineer or scientist who has worked at the frontier and knows how to turn strong models into better products.
Our AI stack spans local robotics and perception models, cloud foundation models, and the runtime connecting them. You’ll own post-training work that improves these systems’ capabilities, reliability, and performance in real use.
The most important qualification is a track record of meaningful results: you’ve built systems that support rapid, rigorous experimentation, achieved convincing state-of-the-art improvements, and shipped those improvements into real products. You lead with evals, treat data as a core part of the work, and bring deep hands-on experience with reinforcement learning.
Own the post-training loop from identifying a capability...
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