Company Overview: Terray Therapeutics is a venture-backed biotechnology company where scientists and engineers build together to transform how small-molecule drugs are discovered and developed. By combining our nanotechnology, experimental science, machine learning, and automation, we leverage Terray’s ability to generate and learn from scale proprietary chemical data to discover novel medicines for patients in need.

At Terray, you will work alongside pioneers and experts in artificial intelligence, chemistry, drug discovery, automation, and nanotechnology in deeply interconnected workflows where computational and experimental teams continuously learn from one another. Our platform, EMMI (Experimentation Meets Machine Intelligence), powers iterative cycles of molecular design and wet-lab experimentation that rapidly improve our understanding of biochemical interactions and guide the next generation of discoveries.

Our teams support both an internal immunology pipeline and partnerships across a broad range of therapeutic areas. The work here is highly collaborative, fast-moving, and grounded in the belief that breakthroughs happen when strong science, thoughtful technology, and great people work closely together.

Position Summary:  Terray Therapeutics is seeking an ML Scientist with a background in reinforcement learning. In this role, you will work to invent and scale cutting-edge systems that discover novel chemical matter and impact real programs. Terray’s machine learning team affords a broad creative scope, and the opportunity to directly affect 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: 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.

Required Qualifications: 

• Strong experience in machine learning, with interest in techniques for sequential decision-making: bayesian and black-box optimization, reinforcement learning.
• Experience with distributed training and inference frameworks.
• Substantial publications (NeurIPS/ICML/AISTATS) or proven record of research contributions.
• Ability to quickly switch between robust engineering and exploration of conceptual insights: the implementation details of training on asynchronous rollouts, and 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.

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: $XXX–$YYY annually, depending on experience. Terray’s salary ranges are designed to be competitive and are benchmarked against market base salary plus bonus within our industry and market. We align pay to role scope, skill level, and impact.

We invest heavily in benefits because taking care of our people matters. Our programs are benchmarked at the top of the Southern California market and designed to provide meaningful support across every stage of life. Benefits include participation in the Company’s stock option plan, a 3% retirement safe harbor contribution, fully paid health, dental, vision insurance for our employees, spouse, partner and families as well as above-market life insurance, disability coverage, and much more to explore during the offer process.