ML Engineer, RL & Autonomous Discovery

<meta><p style="font-family:" basel="" grotesk",arial,sans-serif;font-size:10pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px="" 0px;padding:0px;text-align:justify;"=""><br></p><p style="font-family:" basel="" grotesk",arial,sans-serif;font-size:10pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px="" 0px;padding:0px;text-align:justify;"=""><b><strong style="font-size:10pt;white-space:pre-wrap;">Position Summary: </strong></b><span style="font-size:10pt;white-space:pre-wrap;">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.</span></p><p style="font-family:" basel="" grotesk",arial,sans-serif;font-size:10pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px="" 0px;padding:0px;"=""><span style="font-size:10pt;white-space:pre-wrap;">The key responsibilities of this role are:</span></p><ul data-pattern="discCircleSquare" data-depth="1" style="font-family:" basel="" grotesk",arial,sans-serif;font-size:11pt;font-weight:400;margin:8px="" 0px;line-height:1.6;padding:0px="" 0px="" 32px;list-style-type:disc;"=""><li style="font-size:10pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;"><span style="font-size:10pt;white-space:pre-wrap;">Contribute to</span><b><strong style="font-size:10pt;white-space:pre-wrap;"> </strong></b><span style="font-size:10pt;white-space:pre-wrap;">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.</span></li><li style="font-size:10pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;"><span style="font-size:10pt;white-space:pre-wrap;">Develop synthetic data engines and the inference infrastructure needed to simulate environments for large-scale training.</span></li><li style="font-size:10pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;"><span style="font-size:10pt;white-space:pre-wrap;">Maintain rigorous evaluations to continually monitor the performance of learned policies, using large proprietary datasets collected from internal programs.</span></li></ul><p style="font-family:" basel="" grotesk",arial,sans-serif;font-size:10pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px="" 0px;padding:0px;text-align:left;"=""><br></p><p style="font-family:" basel="" grotesk",arial,sans-serif;font-size:10pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px="" 0px;padding:0px;text-align:left;"=""><b><strong style="font-size:10pt;white-space:pre-wrap;">Experience and Qualifications: </strong></b><span style="font-size:10pt;white-space:pre-wrap;">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. </span></p><p style="font-family:" basel="" grotesk",arial,sans-serif;font-size:10pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px="" 0px;padding:0px;text-align:left;"=""><i><em style="font-size:10pt;white-space:pre-wrap;">Required Qualifications:</em></i><span style="font-size:10pt;white-space:pre-wrap;"> </span></p><ul data-pattern="discCircleSquare" data-depth="1" style="font-family:" basel="" grotesk",arial,sans-serif;font-size:11pt;font-weight:400;margin:8px="" 0px;line-height:1.6;padding:0px="" 0px="" 32px;list-style-type:disc;"=""><li style="font-size:10pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;"><span style="font-size:10pt;white-space:pre-wrap;">Strong experience in machine learning engineering, with interest in techniques for sequential decision-making: bayesian and black-box optimization, reinforcement learning.</span></li><li style="font-size:10pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;"><span style="font-size:10pt;white-space:pre-wrap;">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.</span></li><li style="font-size:10pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;"><span style="font-size:10pt;white-space:pre-wrap;">Experience with the challenges of complex real-world systems and scientific environments, such as expensive queries and experimental noise.</span></li><li style="font-size:10pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;"><span style="font-size:10pt;white-space:pre-wrap;">Appreciation for elegant ideas and what works in practice.</span></li></ul><p style="font-family:" basel="" grotesk",arial,sans-serif;font-size:10pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px="" 0px;padding:0px;text-align:left;"=""><i><em style="font-size:10pt;white-space:pre-wrap;">Preferred Qualifications:</em></i></p><ul data-pattern="discCircleSquare" data-depth="1" style="font-family:" basel="" grotesk",arial,sans-serif;font-size:11pt;font-weight:400;margin:8px="" 0px;line-height:1.6;padding:0px="" 0px="" 32px;list-style-type:disc;"=""><li style="font-size:10pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;"><span style="font-size:10pt;white-space:pre-wrap;">Experience with synthetic data for chemistry, frameworks for autonomous discovery, test-time training.</span></li></ul><p style="font-family:" basel="" grotesk",arial,sans-serif;font-size:10pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px="" 0px;padding:0px;"=""><br></p><p style="font-family:" basel="" grotesk",arial,sans-serif;font-size:10pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px="" 0px;padding:0px;"=""><b><strong style="font-size:10pt;white-space:pre-wrap;">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.</strong></b></p><p style="font-family:" basel="" grotesk",arial,sans-serif;font-size:10pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px="" 0px;padding:0px;text-align:left;"=""><br></p><p style="font-family:" basel="" grotesk",arial,sans-serif;font-size:10pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px="" 0px;padding:0px;text-align:left;"=""><b><strong style="font-size:10pt;white-space:pre-wrap;">Compensation Details:</strong></b><span style="font-size:10pt;white-space:pre-wrap;"> $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.  </span></p>

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