Senior ML Scientist, AI for Protein Engineering
About this role
The AI for Protein Engineering team at Lila Sciences develops and applies generative and predictive models that move biomolecule design programs from in silico hypothesis to wet-lab validated leads. This senior individual contributor role focuses on computational biologics design, sitting at the intersection of machine learning, protein engineering, and therapeutic design.
Daily responsibilities involve active protein engineering programs and new capabilities that improve how the organization designs, evaluates, and learns from biomolecular sequence, structure, and function data. Candidates will collaborate closely with experimental scientists, AI researchers, and platform teams to connect specialist protein design models into a broader autonomous science platform.
Success in this position requires deep machine learning judgment, strong intuition for protein biology, and a proven ability to deliver computationally-designed, wet-lab-validated biologics through artificial intelligence. Individuals who thrive here possess a strong track record in applied research and enjoy translating complex therapeutic questions into well-defined machine learning solutions.
What you will do
- Own applied machine learning workflows for protein engineering campaigns spanning from design specification through experimental learning.
- Develop and adapt methods including de novo generation, sequence- or structure-based property prediction, candidate selection, and active learning.
- Translate therapeutic and biological questions into well-defined machine learning problems, model outputs, and evaluation plans.
- Partner with experimental scientists to interpret why designed biomolecules succeed or fail, and turn those insights into better models.
- Build rigorous evaluation frameworks for model generalization to challenging biologics design problems.
- Integrate specialized design methods into robust software systems and broader reasoning models.
What you need
- Hold a PhD in Computational Biology, Computer Science, Machine Learning, Biophysics, Bioengineering, or a related quantitative field.
- Bring a strong track record applying machine learning to protein design, biologics engineering, or related biomolecular design problems, with industry experience strongly preferred.
- Demonstrate deep machine learning expertise with hands-on experience adapting and developing modern artificial intelligence methods.
- Possess strong intuition for therapeutic biologics design, including sequence, structure, function, developability, and experimental validation considerations.
- Show a demonstrated ability to drive applied research independently from problem definition through experimental validation and iteration.
- Exhibit strong collaboration and communication skills across machine learning, biology, experimental science, and software teams.
Nice to have
- Direct experience designing antibodies, nanobodies, enzymes, peptides, or other therapeutic proteins for applied or clinical pipelines.
- Experience with structure prediction, generative protein design, diffusion models, flow matching, or protein language models in a production research setting.
- Familiarity with structural biology, conformational dynamics, developability, affinity maturation, or other 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 venues.
Benefits
- Medical, dental, and vision coverage
- Employer-paid life and disability insurance
- Flexible time off with generous company wide holidays and paid parental leave
- Educational assistance program
- Commuter benefits including bike share memberships for office based employees
- Company subsidized lunch program
Skills and tech stack
Questions candidates ask
What is the compensation range for this role?
The expected base salary range is $268,000 to $358,000 USD. The company also offers competitive base compensation with bonus potential and generous early-stage equity.
What team will I be working with?
This role is within the AI for Protein Engineering team in Life Sciences AI. You will collaborate with experimental scientists, AI researchers, and platform teams.
What qualifications are required for applicants?
Applicants need a PhD in Computational Biology, Computer Science, Machine Learning, Biophysics, Bioengineering, or a related quantitative field. The role also requires deep machine learning expertise and a strong track record applying machine learning to protein design or related biomolecular design problems.