Principal AI Scientist, Autonomous Discovery

What you'll do

• Design algorithms to evolve candidate populations at scale, maintaining diversity and measuring candidate quality.
• Develop Bayesian frameworks and benchmarks to evaluate populations and compare them with non-iterative baselines.
• Integrate algorithms into autonomous-science pipelines and contribute methods, prototypes, datasets, and publications.

What they ask for

• PhD in a quantitative field or equivalent demonstrated expertise with 5+ years of experience post PhD.
• Deep expertise in advanced Bayesian causal modeling and/or evolutionary programming.
• Ability to invent algorithms and deliver working, validated implementations; strong software engineering and large-scale computational experimentation skills.
• Scientific leadership demonstrated through top-tier publications, influential work, or widely adopted open-source tools.

Nice to have

• Experience applying these methods to biological, scientific, or mechanistic-modeling domains.
• Ability to set technical direction, collaborate across research and engineering, and mentor junior colleagues.

Pay

$208,000 - $286,000 (base salary)

Tools

Bayesian inference, reinforcement learning, optimization, probabilistic programming, genetic/evolutionary algorithms, quality-diversity methods

AI focus

Builds autonomous scientific search systems using evolutionary algorithms and Bayesian evaluation to discover, refine, and rank candidate solutions.