Senior ML Scientist, AI for Protein Engineering

Location: San Francisco, CA, USA

Expected base salary: $268,000–$358,000 USD per year; bonus potential and early-stage equity may also be available.

Company

hirify.global is building AI and automation systems for autonomous scientific discovery across medicine, materials, and energy.

What you will do

• Own applied machine learning workflows for protein engineering campaigns, from design specifications through experimental learning.
• Develop and adapt methods for de novo generation, sequence- and structure-based property prediction, candidate selection, and active learning.
• Integrate protein design methods into robust software systems and broader reasoning models.
• Translate therapeutic and biological questions into well-defined ML problems, model outputs, and evaluation plans.
• Collaborate with experimental scientists to interpret design outcomes and improve models and design principles.
• Build evaluation frameworks for model generalization on challenging biologics design problems.

Requirements

• PhD in Computational Biology, Computer Science, Machine Learning, Biophysics, Bioengineering, or a related quantitative field.
• Strong experience applying machine learning to protein design, biologics engineering, or related biomolecular design problems; industry experience is strongly preferred.
• Deep ML expertise with hands-on experience adapting and developing modern AI methods.
• Strong understanding of therapeutic biologics design, including sequence, structure, function, developability, and experimental validation.
• Ability to lead applied research independently from problem definition through experimental validation and iteration.
• Strong collaboration and communication skills across ML, biology, experimental science, and software teams.

Nice to have

• Experience designing antibodies, nanobodies, enzymes, peptides, or other therapeutic proteins.
• Experience with structure prediction, generative protein design, diffusion models, flow matching, or protein language models.
• Familiarity with structural biology, conformational dynamics, developability, affinity maturation, or related biophysical constraints.
• Experience closing design-test-learn loops with wet-lab teams, including experimental prioritization, high-throughput validation, and active learning.
• Publications, open-source contributions, or applied research outputs in AI for science.

Culture & Benefits

• Full-time U.S. employees receive medical, dental, and vision coverage.
• Benefits include employer-paid life and disability insurance, flexible time off, company-wide holidays, and paid parental leave.
• Educational assistance, commuter benefits, bike share memberships for office-based employees, and a subsidized lunch program are available.
• Employees outside the U.S. receive benefits tailored to their region; international salaries are set to local market.
• Early-stage equity and bonus potential are available.