Computational and Experimental Scientist

ABOUT THE ROLE Join a lean team at an early-stage AI-driven biotech company, combining computational protein design with hands-on experimental work. You will own the design-make-test-model loop, from developing protein design models and operating an inference platform to measuring binding kinetics and feeding results back into improved designs. WHAT YOU'LL DO - Improve and extend discrete diffusion and protein folding models, including model architecture, attention, and hierarchical reasoning. - Operate and refine a biological design inference platform, analyzing usage patterns and diagnosing user behavior. - Write, debug, and operate production protocols for liquid-handling robots and plate automation. - Design and run BLI and SPR assays, fit kinetic curves, conduct quality control, and troubleshoot experimental failure modes. - Develop robust protocols for cloud labs and manage internal screening instrumentation. - Connect receptor biology, protein structure, scoring, and model outputs to practical experimental workflows. - Design sequences, measure their kinetics, use results to update models, and iterate toward improved candidates. WHAT WE'RE LOOKING FOR - 3 to 11 years of relevant experience, including hands-on wet-lab work and experience across biology, bioinformatics, and machine learning. - Experience building or operating protein or biological models, such as discrete diffusion models, protein language models, or structure prediction systems, in an experimental feedback loop. - Practical experience with tools such as ESM, ProtT5, RFdiffusion, BindCraft, or equivalent, applied to real protein design workflows. - Hands-on experience writing and debugging liquid-handler protocols and personally fitting BLI or SPR kinetic curves end to end. - Proficiency with Python or equivalent scripting for robot automation and kinetic data analysis. - Knowledge of receptor biology, protein structure, and scoring, with the ability to diagnose why computational predictions may not match experimental results. - Experience in biotech or life sciences software customer success, solutions engineering, or a similar customer-facing operator role, including user conversion and varied purchasing processes. - Ability to conduct personalized scientific outreach and experience operating inference systems at scale or refining model architectures. COMPENSATION & BENEFITS Consulting engagement: $3,000 to $5,000 per month. Full-time conversion base salary: $80,000 to $200,000 annually, depending on experience, with equity and deal-contingent upside. Visa sponsorship is not available. LOCATION Hybrid in New York, United States, with increased on-site presence expected as internal screening operations are established.