Co-Op, ML Scientist for Protein Engineering

Lila Sciences
Lila Sciences logo
Location
San Francisco, CA USA
Job Type
Full-time
Posted
August 10, 2026
Views
6

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.

About LILA

Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.

LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.

Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.

We’re All In

Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.

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.

About LILA

Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.

LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.

Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.

We’re All In

Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.

Information you provide during your application process will be handled in accordance with ourCandidate Privacy Policy.

A Note to Agencies

Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.

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Frequently Asked Questions

Where is the job located, and is it remote/hybrid/on-site?
The job is located in San Francisco, CA USA. The posting does not specify a remote or hybrid work-mode policy.
What are the required qualifications and experience level for this co-op?
You must be a current PhD student in Computer Science, Machine Learning, Computational Biology, Bioengineering, Biophysics, or a related quantitative field. You also need research experience in ML, computational biology, or protein engineering, strong Python skills, and experience with frameworks like PyTorch or JAX.
What are the key responsibilities of the ML Scientist Co-Op?
You will contribute to ML research for protein engineering and antibody design, explore generative and predictive modeling, translate biological goals into computational problems, analyze datasets, prototype workflows connecting predictions to wet-lab feedback, and communicate results to collaborators.
Who will I be working with and what is the team environment like?
You will work alongside Lila scientists and ML researchers on the Life Science AI team. You will collaborate in a fast-moving, cross-disciplinary research environment, partnering with experimental scientists to connect computational design to real experimental needs.

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Job Information

Source: manual
AI Relevance: 92/100 (Highly relevant)
Remote Type: onsite
Allowed Locations: Worldwide
Skills & Tags:
lila sciences machine learning computational biology protein

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