Role overview
- Contribute to RL frameworks that drive the design-make-test-analyze (DMTA) cycles that power our EMMI platform, which coordinates a closed-loop between a highly automated lab and our reward models.
- 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, using large proprietary datasets collected from internal programs.
Experience and Qualifications:
Part of Terray’s success is nurtured by a hands-on work environment where everyone is accountable, vested in a vision of excellence, and actively taking part in the success of the business. Terray supports a positive work environment where employees can feel engaged, recognized and empowered to be creative.
What we're looking for
- Experience with synthetic data for chemistry, frameworks for autonomous discovery, test-time training.
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.
Compensation Details:
$147,000 - 227,850 (annually) depending on experience; participation in the Company's option plan; 3% retirement safe harbor contribution; fully-paid medical, dental, vision, life and disability benefits and much more.