Principal Scientist, Evolutionary Search & Autonomous Discovery
Core
Designing and implementing algorithms for autonomous systems to explore large scientific and computational search spaces by evolving populations of models, hypotheses, or programs.
Role type
Senior Principal Scientist (Evolutionary Search & Autonomous Discovery)
Builds
Autonomous science platforms capable of iterative search, selection, variation, and evaluation of candidate solutions.
Domain
Artificial Intelligence, Evolutionary Computation, Automated Scientific Discovery
Deliverable
production ML models
Required skills
Advanced Bayesian causal modeling, Evolutionary programming, Probabilistic programming, Bayesian model comparison, Genetic/evolutionary algorithms, Quality-diversity methods, Population-based search, Novel algorithm design from first principles, Large-scale parallel/distributed execution
Preferred skills
Biological or mechanistic-modeling domain experience, Scientific leadership with first-author publications at top-tier venues (NeurIPS, ICML, ICLR)
Technologies
Bayesian inference frameworks, Reinforcement learning libraries, Optimization toolkits
Responsibilities
Design algorithms for autonomously generating and evolving candidate solution populations; Develop methods to maintain population diversity and prevent solution collapse; Build probabilistic frameworks for evaluating candidates against heterogeneous evidence; Define quantitative metrics for diversity and quality; Integrate algorithms into closed-loop autonomous science pipelines; Establish benchmarks to validate performance over baselines; Contribute to scientific direction via publications and reusable methods
