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How much do protein engineering jobs pay per year?

As of Jul 21, 2026, the average yearly pay for protein engineering in the United States is $62,977.00, according to ZipRecruiter salary data. Most workers in this role earn between $47,000.00 and $72,000.00 per year, depending on experience, location, and employer.

What is a Protein Engineering job?

A Protein Engineering job involves designing, modifying, and optimizing proteins for specific applications in medicine, biotechnology, and industry. Scientists in this field use computational modeling, directed evolution, and genetic engineering to enhance protein functions. Roles may include developing enzymes for drug production, improving therapeutic proteins, or creating biomaterials. This work often requires expertise in molecular biology, biochemistry, and structural biology.

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

To thrive in Protein Engineering, you need a solid background in biochemistry, molecular biology, and genetic engineering, typically supported by a relevant advanced degree. Experience with laboratory techniques such as PCR, site-directed mutagenesis, protein expression/purification, and analytical tools like mass spectrometry or chromatography is essential. Strong problem-solving abilities, attention to detail, and effective teamwork and communication skills help you stand out in this role. These competencies are crucial for designing novel proteins, collaborating in multidisciplinary teams, and ensuring successful project outcomes in biotech or pharmaceutical environments.

What are typical daily responsibilities for someone working in Protein Engineering?

A typical day in Protein Engineering involves designing and conducting experiments to modify protein sequences, expressing and purifying proteins, and analyzing their structure or function using techniques like chromatography and spectroscopy. You will often document your results, troubleshoot unexpected findings, and present data to colleagues or project teams. Collaboration with scientists in molecular biology, bioinformatics, and structural biology is common to ensure robust experimental design and data interpretation. The role may also include literature reviews to stay updated with the latest research and participating in team meetings to plan next steps or project timelines.

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Protein Engineering jobs near you
Infographic showing various Protein Engineering job openings in the United States as of July 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $62,977 per year, or $30.3 per hour.

Co-Op, LS AI, ML Scientist for Protein Engineering

Lila Sciences

San Francisco, CA

Other

Posted 22 days ago

Job description

Your Impact at LILA

Lila is embarking on a transformative mission to redefine the future of medicine by combining automated large-scale data generation with scientific superintelligence. At Lila, we don't just use AI to analyze biology; we are building the loop where AI and automation co-evolve to solve the hardest problems in medicine.

To this end, the Life Science AI team is developing machine learning systems that can reason over biological data and help design better biomolecules. We are seeking an ML Scientist Co-Op to contribute to protein engineering research, including problems related to generative protein design, antibody engineering, developability, and wet-lab-informed model iteration.

This is an opportunity to work alongside Lila scientists on applied ML research at the interface of AI and biology. You will help explore models, datasets, and workflows that connect computational protein design ideas to real experimental needs, gaining hands-on experience in a fast-moving scientific environment.

What You'll Be Building

  • Contribute to ML research projects focused on protein engineering, antibody design, and related biomolecule design problems.
  • Explore generative and predictive modeling approaches for protein sequence, structure, function, and developability.
  • Work with scientists and ML researchers to translate biological design goals into tractable computational problems.
  • Analyze biological and experimental datasets to identify patterns, evaluate model outputs, and guide design decisions.
  • Prototype workflows that connect model predictions, candidate prioritization, and wet-lab feedback.
  • Communicate results clearly through code, notebooks, written summaries, and presentations to scientific and technical collaborators.

What You'll Need to Succeed

  • Currently enrolled as a PhD student in Computer Science, Machine Learning, Computational Biology, Bioengineering, Biophysics, or a related quantitative field.
  • Research experience in machine learning, computational biology, protein engineering, or a closely related area.
  • Strong programming skills in Python and experience with modern ML frameworks such as PyTorch, JAX, or similar tools.
  • Ability to work with biological sequence, structure, assay, or other scientific datasets.
  • Interest in applying ML methods to real biological design problems in partnership with experimental scientists.
  • Clear communication skills and comfort working in a collaborative, cross-disciplinary research environment.

Bonus Points For

  • Experience with protein language models, structure prediction, generative protein design, diffusion or flow-based models, or antibody design.
  • Familiarity with protein structure, biophysics, developability, affinity maturation, or wet-lab validation concepts.
  • Publications, preprints, open-source work, or research projects in ML for biology, protein engineering, or AI for Science.
  • Experience building active learning, model evaluation, or data analysis workflows for scientific discovery.
  • Comfort collaborating with experimental scientists and translating between ML concepts and biological constraints.

Apply