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$73K

$194.7K

$254K

How much do director protein engineering jobs pay per year?

As of Jun 17, 2026, the average yearly pay for director protein engineering in the United States is $194,709.00, according to ZipRecruiter salary data. Most workers in this role earn between $141,500.00 and $253,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Director Protein Engineering position, and why are they important?

A Director Protein Engineering typically requires a PhD in biochemistry, molecular biology, or related fields, along with extensive experience in protein engineering, assay development, and team leadership. Familiarity with industry-standard tools like molecular modeling software, high-throughput screening systems, and relevant regulatory compliance certifications is important. Exceptional project management, communication, and strategic decision-making skills help in guiding cross-functional teams and driving innovation. These competencies ensure effective oversight of research programs, facilitate collaboration, and enable advanced problem-solving to achieve organizational goals.

What does a Director of Protein Engineering do?

A Director of Protein Engineering leads teams in designing, optimizing, and developing proteins for various applications, such as therapeutics, biotechnology, or industrial enzymes. They oversee research strategies, manage cross-functional collaborations, and ensure project goals align with company objectives. This role requires expertise in molecular biology, structural biology, and protein design, along with strong leadership and project management skills.

What are the main responsibilities of a Director Protein Engineering on a day-to-day basis?

A Director Protein Engineering typically oversees project pipelines, sets scientific strategy, and ensures timely delivery of research milestones. You can expect to manage and mentor multidisciplinary teams, coordinate with departments such as discovery biology, analytical chemistry, and process development, and communicate progress to senior leadership. Daily activities often include troubleshooting experimental challenges, allocating resources, and staying updated on the latest advances in protein engineering. The role also involves fostering an innovative work environment and ensuring research aligns with business objectives.

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Director Protein Engineering jobs near you

Senior / Principal Scientist, AI for Protein Engineering

Lila Sciences

San Francisco, CA • On-site

Other

Posted 13 days ago

Job description

Your Impact at LILA

Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Sciences AI (LSAI), the AI for Protein Engineering team develops and uses the generative and predictive models that drive Lila's biomolecule design programs from in silico hypothesis to wet-lab validated lead.

We are seeking a Senior or Principal Scientist to join this team as a senior IC focused on antibody design and engineering. You will develop and execute the methods and workflows that ensure successful completion of antibody campaigns. Scope may expand to additional modalities such as enzymes and peptides as needs evolve.

This role sits at the bilingual edge of ML and biology. You will own biological understanding of campaign needs and partner closely with the Life Science Research team to design and validate computational predictions in the lab. You will shape the technical agenda for AI protein engineering at Lila and represent that work both internally and to the broader research community.

What You'll Be Building

  • Develop and own protein design and engineering workflows for antibody campaigns, including de novo design, affinity maturation, and developability optimization
  • Execute design workflows end-to-end for active campaigns and deliver wet-lab-validated leads against program milestones
  • Translate campaign requirements - epitope selection, affinity targets, biophysical constraints, and developability criteria - into well-defined ML problems and design specifications
  • Adapt and extend state-of-the-art AI methods (generative models, protein language models, structure-conditioned design) to the specific demands of antibody and broader biomolecule engineering
  • Partner with the Life Science Research team on design validation, building active learning loops where wet-lab data refines and improves model performance
  • Expand the protein engineering platform to additional modalities such as enzymes and peptides as needs evolve

What You'll Need to Succeed

  • PhD in Computational Biology, Computer Science, Machine Learning, Biophysics, or a related quantitative field
  • Proven track record of successful design of wet-lab-validated biomolecules through AI, with industry experience strongly preferred
  • Deep ML expertise with the ability to modify and adapt state-of-the-art AI approaches for protein engineering, not just apply them off-the-shelf
  • Strong fluency across both ML and protein biology, with hands-on understanding of antibody design
  • Demonstrated ability to drive a research and engineering program independently, from problem definition through experimental validation and iteration
  • Track record of close collaboration with experimental scientists and clear communication across the ML/biology boundary

Bonus Points For

  • Direct experience designing antibodies, nanobodies, or other therapeutic proteins for clinical or therapeutic pipelines
  • Experience with structure prediction, generative protein design (diffusion, flow-matching, or similar), and protein language models in a production research setting
  • Experience in structural biology and conformational dynamics
  • Experience extending design methods to additional modalities such as enzymes, peptides, or other engineered biomolecules
  • High-impact publications or open-source contributions in AI for Science (NeurIPS, ICML, ICLR, Nature Methods, Nature Biotechnology, or equivalent)
  • Experience designing or operating active learning loops between computational design and high-throughput experimental validation

Apply