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.