Computational and Experimental Scientist

ABOUT THE ROLE Join a lean team at an early-stage AI-driven biotech company, owning computational and experimental work across protein design, laboratory testing, and model improvement. You will close the design-make-test loop, translating sequences into kinetics data and using results to improve the next round of designs. WHAT YOU'LL DO - Improve discrete diffusion and companion folding models for peptide and protein design, including architecture refinements and hierarchical reasoning. - Operate and refine an inference platform, investigate usage patterns, and help scientists apply its outputs. - Write, debug, and maintain production protocols for liquid-handling robots and plate automation. - Run BLI and SPR workflows end to end, from assay design and immobilization through kinetic fitting and quality control. - Develop clear protocols for external laboratories and operate internal screening instrumentation. - Connect receptor biology, protein structure, scoring, and experimental results to identify failure modes and improve designs. - Support scientific platform users and customer workflows, including personalized technical outreach and navigating biotech and pharma purchasing processes. WHAT WE'RE LOOKING FOR - Typically 3 to 11 years of relevant experience, including experience in biotech or life sciences software and customer-facing technical or commercial work. - Hands-on wet-lab experience and a background in biology, biochemistry, or a related life science. - Experience building or operating protein design, protein language, discrete diffusion, or structure prediction models in an experimental design-make-test cycle. - Practical use of tools such as ESM, ProtT5, RFdiffusion, BindCraft, or equivalents. - Personally developed and debugged production methods for Hamilton, Tecan, Opentrons, or equivalent liquid-handling platforms. - End-to-end BLI or SPR kinetic fitting experience, including diagnosing assay and measurement failure modes. - Python or equivalent scripting skills for automation and kinetic data analysis, plus an understanding of receptor biology, protein structure, and scoring. - Experience operating inference systems at scale, improving model architectures, and working across experimental biology, bioinformatics, and machine learning. COMPENSATION & BENEFITS Consulting compensation is $3,000 to $5,000 per month. Full-time conversion offers a base salary of $80,000 to $200,000 annually, depending on experience, along 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.