About the role
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.
Originally posted on Himalayas
