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How much do protein engineer jobs pay per year?

As of Jun 30, 2026, the average yearly pay for protein engineer in the United States is $92,020.00, according to ZipRecruiter salary data. Most workers in this role earn between $63,000.00 and $116,000.00 per year, depending on experience, location, and employer.

What are the typical daily tasks and projects for a Protein Engineer?

Protein Engineers typically spend their days designing and conducting experiments to modify and improve protein characteristics, analyzing data from assays, and collaborating with cross-functional teams such as molecular biologists and biochemists. Their work often involves using specialized laboratory equipment, computational modeling tools, and maintaining detailed records of their experimental results. Projects may include developing enzymes for industrial processes, optimizing therapeutic proteins, or engineering innovative biomaterials. The pace can be dynamic, with a mix of independent bench work and group meetings to discuss progress, troubleshoot challenges, and plan next steps. This collaborative environment offers opportunities to learn from peers and contribute to cutting-edge scientific advancements.

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

To thrive as a Protein Engineer, you need expertise in molecular biology, biochemistry, and protein structure-function analysis, often supported by an advanced degree in a relevant scientific field. Familiarity with tools such as protein modeling software, high-throughput screening systems, and genetic engineering techniques is commonly required. Attention to detail, strong analytical thinking, and effective teamwork are valuable soft skills in this role. These abilities are crucial for designing, optimizing, and testing proteins with desired properties, ensuring impactful contributions to research or product development.

What does a Protein Engineer do?

A Protein Engineer designs and modifies proteins for specific functions in fields like medicine, biotechnology, and bioengineering. They use computational modeling, molecular biology techniques, and biochemical assays to improve protein stability, activity, or binding properties. Their work is crucial for developing new therapeutics, enzymes, and biomaterials for various industries.

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Protein Engineer jobs near you
Infographic showing various Protein Engineer job openings in the United States as of June 2026, with employment types broken down into 95% Full Time, and 5% Part Time. Highlights an 95% In-person, and 5% Remote job distribution, with an average salary of $92,020 per year, or $44.2 per hour.

Senior / Principal Scientist, AI for Protein Engineering

Lila Sciences

San Francisco, CA

Other

Posted 26 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