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Lila Sciences

ML Scientist I / II, Foundation Models for Life Sciences

Lila Sciences · San Francisco, CA USA

Posted about 2 months ago

or apply directly on Lila Sciences's site. We never take the application ourselves.

Is this posting real?

This role has been open
54 days
Lila Sciences's roles stay open a median of 55 days
Reposted
No
Salary listed
No
0% of Lila Sciences's roles list one
Ghost-job risk at Lila Sciences
high
104 stale, 2 reposted of 118 open
Hiring momentum
155 roles opened in the last 90 days
↑ up vs. the prior 90 days
Last confirmed on the employer's board
2026-09-27

Measured from postings appearing on and disappearing from Lila Sciences's own greenhouse board since 2026-08-03. Full hiring picture for Lila Sciences.

About this role

As a Scientist I or II at Lila, you will focus on structure prediction and co-folding, particularly in protein–protein interactions to aid in antibody and biologics design. Your role involves training and evaluating models, collaborating with experimental scientists, and contributing to the development of Lila's AI platform for scientific discovery. You will also engage in the end-to-end machine learning process, shaping data strategies and ensuring model performance through feedback loops with experimental results.

Our read on this posting2.8out of 5
benefits
4/5
freshness
1/5
career value
5/5
role clarity
4/5
pay transparency
0/5

Scored from the posting itself — how clearly the role is described, how much it says about pay and benefits, and how recently it was listed. Not a judgement of Lila Sciences as an employer.

What you need

  • PhD in Computer Science, Machine Learning, Computational Biology, Biophysics, or a related quantitative field (or Master's with equivalent research experience)
  • Hands-on experience training deep learning models on molecular, protein, or structural data
  • Strong foundation in generative model architectures and training
  • Ability to formulate and execute research independently
  • Familiarity with at least one life science domain (structural biology, protein engineering, molecular biology, genomics, or related)
  • Experience collaborating with experimental scientists or working with biological/chemical data

Nice to have

  • Experience training or extending co-folding, structure prediction, protein–protein, or diffusion deep learning models
  • Experience with AlphaFold or AlphaFold-derived methods
  • Antibody, biologics, or protein design experience
  • Familiarity with distributed training infrastructure and large-scale scientific data pipelines
  • Contributions to open-source ML tools, frameworks, or benchmark datasets for scientific applications

What you get

  • Competitive base compensation with bonus potential and generous early-stage equity
  • Comprehensive benefits program including medical, dental, and vision coverage
  • Employer-paid life and disability insurance
  • Flexible time off with generous company-wide holidays
  • Paid parental leave
  • Educational assistance program

Worth weighing

  • No specific mention of the exact technologies or methodologies used in the team's current projects beyond general terms
  • The role requires close collaboration with experimental scientists, which may require strong communication skills and adaptability
  • The position is described as an individual contributor role, which may not suit those looking for more collaborative or team-oriented environments

Summarised from Lila Sciences's posting. Read the full original.

Listed by Lila Sciences on their greenhouse job board, last confirmed open on 2026-09-27. PitchMeAI is not the employer.

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