Research Scientist; pre & post-training

What if AI systems could run full research loops — not just generate outputs, but form hypotheses, design experiments, and produce new scientific insight?

This team is building autonomous AI scientists that do exactly that. Their systems ingest large bodies of scientific literature, reason across them, and generate traceable outputs already used by teams in life sciences.

The problem is no longer getting models to produce plausible answers. It’s pushing them to plan, explore, and iterate across complex domains — reliably, and at scale.

You’ll join a team working at the edge of this shift, developing models that move beyond instruction following into structured, multi-step scientific reasoning.

This is not research in isolation. Your work will be deployed into real systems used by scientists, where model behaviour directly impacts what the platform can discover.

You’ll work closely with engineers and domain experts across biology and chemistry, translating open-ended problems into systems that can be trained, evaluated, and improved in production.

The company originated from one of the earliest groups working seriously on AI for science, including early language agents and AI-generated discoveries.

They’re now pushing further with systems capable of long-horizon reasoning across huge amounts of data.

They’ve primarily focused on post-training and reasoning so far, and are now moving into pre-training their own models to support this end-to-end.

• Developing models that can reason across long-horizon scientific problems
• Designing post-training methods to improve multi-step decision making
• Working on sampling, exploration, and evaluation in complex environments
• Building systems that move from research ideas into production workflows
• Collaborating with scientists to define problems and validate outputs

• Strong background in machine learning research (RL, representation learning, or related areas)
• Experience pre-training or post-training LLMs
• Track record of applying ML to real-world or complex domains
• Strong programming skills (PyTorch, JAX, or similar)
• Ability to work across research and applied systems

• Work on systems that aim to automate scientific discovery
• Direct impact on real-world research and outcomes
• Small, high-calibre team across AI and science
• Real traction, not just prototypes

San Francisco (on-site or hybrid). Other locations considered: NYC, London.