Contribute to RL frameworks that drive the design-make-test-analyze (DMTA) cycles
Develop synthetic data engines and the inference infrastructure needed to simulate environments for large-scale training
Maintain rigorous evaluations to continually monitor the performance of learned policies
Requirements:
Strong experience in machine learning engineering
interest in techniques for sequential decision-making: bayesian and black-box optimization, reinforcement learning
Ability to quickly switch between robust engineering and exploration of conceptual insights
Experience with the challenges of complex real-world systems and scientific environments
Appreciation for elegant ideas and what works in practice.
Only applicants with github, proof of relevant work, or a one-page writeup of experience applying autonomous discovery to a scientific problem that is verifiable will be considered.
Benefits:
fully-paid medical, dental, vision, life and disability benefits