Lead AI Protein Design Engineer

Stealth BioTech Startup; Boston (MA); On-site; USD 120,000 - 180,000; Full time

Job summary

Stealth BioTech Startup in Boston, MA seeks an AI Protein Engineer to lead the design and engineering of protein capabilities using AI tools to generate and rank designs for new proteins and modifications.

This fully computational role collaborates with the technical team to translate scientific questions into computational approaches, assess performance, and create documented, reproducible workflows used by researchers.

Qualifications

• Degree in a quantitative field and 3+ years with a master’s or 5+ years with a bachelor’s. Academic research counts.
• Proven ability to lead computational projects from open-ended scientific questions to models or tools used by others, with ownership of key technical decisions.
• Experience evaluating models using baselines, held-out data, data-leakage checks, sensitivity analysis, and quantitative uncertainty estimates.
• Experience benchmarking scientific software and computational methods for accuracy, reliability, computational cost, and scientific relevance, and making adoption recommendations.
• Experience delivering tested, documented, version-controlled workflows whose results other researchers have successfully reproduced.

Responsibilities

• Lead the development of our AI-driven protein design and engineering capability.
• Translate scientific questions into computational approaches, evaluate their performance, and turn successful approaches into tested, documented, reproducible workflows.

Skills

Computational models

Lead computational projects

Model evaluation

Benchmarking software

Reproducible workflows

Education

Master’s or PhD in quantitative field

Bachelor’s degree in quantitative field

Job description

Stealth BioTech Startup in Boston, MA seeks an AI Protein Engineer to lead the design and engineering of protein capabilities using AI tools to generate and rank designs for new proteins and modifications.

This fully computational role collaborates with the technical team to translate scientific questions into computational approaches, assess performance, and create documented, reproducible workflows used by researchers.