ML Engineer, RL & Autonomous Discovery

<meta><p style="font-family:&quot;Basel Grotesk&quot;,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:&quot;Basel Grotesk&quot;,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:&nbsp;</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:&quot;Basel Grotesk&quot;,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:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 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:&quot;Basel Grotesk&quot;,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:&quot;Basel Grotesk&quot;,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:&nbsp;</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,&nbsp;recognized&nbsp;and empowered to be creative.&nbsp;</span></p><p style="font-family:&quot;Basel Grotesk&quot;,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;">&nbsp;</span></p><ul data-pattern="discCircleSquare" data-depth="1" style="font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 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:&quot;Basel Grotesk&quot;,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:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 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:&quot;Basel Grotesk&quot;,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:&quot;Basel Grotesk&quot;,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:&quot;Basel Grotesk&quot;,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:&quot;Basel Grotesk&quot;,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;">&nbsp;$147,000 - 227,850&nbsp;(annually) depending on experience; participation in the Company's option plan; 3%&nbsp;retirement safe harbor&nbsp;contribution;&nbsp;fully-paid&nbsp;medical, dental, vision,&nbsp;life&nbsp;and disability&nbsp;benefits and much more.&nbsp;&nbsp;</span></p> <meta><p style="font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;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;">Company Overview:</strong></b><span style="font-size:10pt;white-space:pre-wrap;"> Terray Therapeutics is a venture-backed biotechnology company led by pioneers and long-time leaders in artificial intelligence, synthetic chemistry, automation, and nanotechnology.&nbsp;We’re&nbsp;generating chemical data purpose-built to propel drug discovery into the information age — and&nbsp;we’re&nbsp;doing it on a larger scale and faster than has ever before been possible.&nbsp;</span></p><p style="font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;text-align:justify;"><span style="font-size:10pt;white-space:pre-wrap;">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.&nbsp;</span></p>