Role OverviewWe are hiring research scientists to improve the real-world performance of our foundation models. The work involves planning targeted data generation campaigns, designing evals that accurately measure model performance, and formulating post-training recipes aimed at making models useful for real drug discovery problems.
What You Will Do
You will work in tight loops with our pre-training researchers, research engineers and science team. Your work will ship fast and you'll see your results reach real therapeutic programs across industry.
Why It Might Be a Fit
We look for technical depth paired with interest in the underlying biology. Biological background is not required. All of the following are positive signals: Published research in machine learning for protein, antibody, or small-molecule modelling.
Requirements
- Published research in machine learning for protein, antibody, or small-molecule modelling.
- Fluency in the technical details of protein language, structure prediction, and design models (AlphaFold, RFDiffusion, ProteinMPNN, ESM, etc).
- Strong quantitative background (CS, math, or physics), particularly in probability, statistics, and machine learning.
- Deep software engineering expertise, with experience working in large codebases.
Benefits
- The opportunity to work at the vanguard of AI research and frontier biology, with world-class people, on a mission that matters.
- We protect and promote a culture of high velocity and ownership.
- We compensate our team accordingly.
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