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.