ML Engineer, RL & Autonomous Discovery at Terray Therapeutics

, , United States -

Full Time

Start Date

Immediate

Expiry Date

18 Jul, 26

Salary

227850.0

Posted On

19 Apr, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine learning engineering, Reinforcement learning, Bayesian optimization, Black-box optimization, Synthetic data, Inference infrastructure, Policy evaluation, Sequential decision-making, Asynchronous rollouts, Chemistry, Autonomous discovery, Test-time training

Industry

Biotechnology Research

Description

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: 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 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.

Responsibilities

You will contribute to reinforcement learning frameworks that power the automated design-make-test-analyze cycles of the EMMI platform. Additionally, you will develop synthetic data engines and inference infrastructure to support large-scale training and policy evaluation.