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.