Company Overview: Terray Therapeutics is a venture-backed biotechnology company led by pioneers and long-time leaders in artificial intelligence, synthetic chemistry, automation, and nanotechnology. We’re generating chemical data purpose-built to propel drug discovery into the information age — and we’re doing it on a larger scale and faster than has ever before been possible. Our closed loop system generates precise chemical datasets at unrivaled scale that work seamlessly with AI to systematically map biochemical interactions between small molecules and causes of disease. Iterative cycles of virtual molecular design and experimentation power AI and machine learning models, which in turn guide the next cycle of design. With a chemistry engine that measures billions of interactions daily and becomes increasingly precise with every cycle, we can answer an unprecedented array of questions — deriving insights that enable us to predictably create drugs for patients in need. Position Summary: Terray Therapeutics is seeking a ML Engineer to contribute to the automated discovery engine of our closed-loop platform. In this role, you will work to invent and scale cutting-edge systems that discover novel chemical matter and impact real programs. The key responsibilities of this role are: 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: Required Qualifications: Strong experience in machine learning engineering, with 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, e.g., implementation details of training on asynchronous rollouts while understanding why policy divergence leads to instabilities. Experience with the challenges of complex real-world systems and scientific environments, such as expensive queries and experimental noise. Appreciation for elegant ideas and what works in practice. Preferred Qualifications: 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.