---
title: 'Scientist II / Senior ML Scientist, Cofolding and Structure-Aware ML at Lila Sciences'
canonical: 'https://feeny.ai/job/scientist-ii-senior-ml-scientist-cofolding-and-structure-aware-ml-lila-sciences-znx590hbth3g'
type: 'job'
last_seen: '2026-09-11'
---

# Scientist II / Senior ML Scientist, Cofolding and Structure-Aware ML at Lila Sciences

- **Company:** Lila Sciences
- **Location:** Cambridge, MA / London, United Kingdom / San Francisco, CA
- **Compensation:** $228k–$358k
- **Posted:** 2026-08-03
- **Last confirmed live:** 2026-09-11
- **Apply:** https://job-boards.greenhouse.io/lilasciences/jobs/4340151009

## Job description

## Your Impact at LILA

Lila Sciences is seeking a Machine Learning Scientist, Cofolding and Structure-Aware ML to train next-generation cofolding models for drug discovery. This role is focused on improving models that reason over proteins, ligands, binding context, and experimental data, potentially using contrastive learning and related representation-learning approaches.

This person should have direct experience training modern scientific ML models, not only using pretrained systems. You will work with ML researchers, computational chemists, computational biophysicists, data engineers, and drug discovery teams to develop models that learn from DEL and related datasets, connect molecular and protein context, and improve AI-driven discovery decisions.

The models developed in this role should produce outputs that medicinal and computational chemists as well as biophysicists can interrogate, validate, and use in downstream agent-driven discovery decisions.

## What You'll Be Building

- Train and evaluate cofolding models for protein-ligand and related molecular discovery applications.
- Use contrastive learning, representation learning, self-supervised learning, or related methods where they help improve cofolding models trained on molecules, proteins, structures, and experimental readouts.
- Develop modeling approaches that make DEL data more useful for learning binding, enrichment, selectivity, and structure-activity signals.
- Build and evaluate models informed by Boltz, AlphaFold-style cofolding, equivariant GNNs, and related structure-aware ML methods.
- Design training objectives, including contrastive, self-supervised, or multimodal objectives, that connect ligands, proteins, structures, assays, simulations, and experimental data.
- Build rigorous evaluation frameworks that distinguish meaningful molecular learning from dataset artifacts, leakage, or spurious correlations.
- Collaborate with data and platform teams to define datasets, labels, negatives, controls, and metadata needed for model training.
- Partner with computational chemistry and biophysics teams to connect model outputs to physically and chemically meaningful hypotheses.
- Work with low-data learning scientists to identify which DEL, assay, simulation, or structural data would most improve model performance in focused chemical spaces.
- Work with research engineers to scale training, inference, and evaluation workflows.
- Help expose trained models and model-derived capabilities as tools for scientists and AI agents.

## What You'll Need to Succeed

- PhD or equivalent experience in machine learning, computational biology, computational chemistry, bioinformatics, computer science, or a related field.
- Hands-on experience training deep learning models for molecular, protein, structural biology, or scientific data applications.
- Experience with contrastive learning, representation learning, self-supervised learning, or multimodal learning.
- Familiarity with DEL or related selection, enrichment, screening, or molecular assay datasets.
- Experience with protein-ligand modeling, cofolding, structure prediction, geometric deep learning, or structure-aware molecular ML.
- Practical experience with PyTorch, JAX, or an equivalent ML framework.
- Ability to design careful experiments, ablations, and evaluations for scientific ML models.
- Strong understanding of data quality, leakage risks, negative construction, and benchmark design.
- Ability to collaborate across ML, data, computational science, and drug discovery functions.

Bonus Points For

- Hands-on experience with DEL data.
- Drug discovery experience, especially in protein-ligand modeling or molecular optimization contexts.
- Experience with Boltz, AlphaFold or AlphaFold-derived methods, equivariant GNNs, diffusion models, protein language models, or molecular encoders.
- Experience training or extending cofolding, protein-ligand, protein-protein, structure prediction, diffusion, or geometric deep learning models.
- Experience with distributed model training and large-scale scientific data pipelines.
- Familiarity with active learning or closed-loop molecular design.
- Experience integrating ML models into agentic scientific workflows.

## Compensation

We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.

U.S. Benefits. Full-time U.S. employees receive a 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; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.

International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.

Expected Base Salary Range $228,000—$358,000 USD

## 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 our [Candidate Privacy Policy](https://www.lila.ai/candidate-privacy-policy-notice).

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.

## About Lila Sciences

## Company Overview
- **One-liner**: Lila Sciences is building the world’s first Scientific Superintelligence platform and autonomous lab, using AI to autonomously generate hypotheses, design and run experiments, and learn from results in real time across life sciences, chemistry, materials, energy, and defense.
- **Entity Type**: Private (Privately Held) – Series A, Seed, and Grant funding rounds
- **Headquarters**: Cambridge, Massachusetts, United States
- **Founded**: Not publicly available (first funding round was Seed in March 2025)
- **Founders**: Not publicly available (founded within Flagship Pioneering’s ecosystem)

## Core Business
- **Primary industry**: AI-powered scientific discovery and autonomous laboratory platforms
- **Target customers**: B2B – research organizations, biotech, pharmaceutical, energy, materials, aerospace, and defense companies
- **Mission**: “Scientific Superintelligence to solve humankind’s greatest challenges” – accelerating discovery across medicine, materials, energy, and defense

## Products & Services
- **LILA Platform (Scientific Superintelligence)**: An AI model and operating system that autonomously executes the entire scientific method – generating hypotheses, designing experiments, running them in physical labs, and learning from results in real time.
- **AI Science Factory™ Instruments**: Proprietary hardware and robotics that serve as the “body” of the platform, enabling automated experimentation at scale.
- **Domain-specific applications**: Tailored solutions for biotech (drug discovery, protein engineering), therapeutics (mRNA, antibodies, cell therapies), energy & environment (clean fuels, catalysis, critical minerals), advanced materials, chemicals, aerospace & defense, and oil & gas.

## Market Standing
- **Valuation**: Not disclosed
- **Key Metric**: Total funding – USD $550.7M (as of LinkedIn data)
  - Seed Round (March 2025): $200M led by Flagship Pioneering
  - Series A (September 2025): $235M led by Braidwell and Collective Global Management
  - Series A (November 2025): $115M led by NVentures (NVIDIA)
  - Grant (January 2026): $671,400 led by ARIA
- **Notable Investors/Partners**: Flagship Pioneering, NVIDIA (NVentures), Braidwell, Collective Global Management, ARIA
- **Growth Signals**: Named #25 on the 2026 CNBC Disruptor 50 List; headcount of 308 employees (monthly growth +10.6%); 122 active job postings; operates in 6 countries (US, UK, Canada, Poland, Spain, Germany)

## Competitive Advantages
- **Proprietary AI model** that consistently outperforms other models across scientific domains in complex analysis and reasoning.
- **Autonomous physical labs** that close the loop between AI hypothesis generation and real-world experimentation.
- **“Team of Teams” operating model** enabling startup speed at scale while maintaining radical transparency and high trust.
- **General platform approach** (inspired by Rich Sutton’s “Bitter Lesson”) rather than narrow domain-specific tools, allowing broad applicability.

## Strategic Focus
- Accelerating discovery across medicine, materials, energy, and defense
- Scaling the autonomous science platform to more industries and use cases
- Building “Scientific Superintelligence” that can tackle humanity’s greatest challenges
- Continued investment in AI research, robotics, and lab automation

## Why Work Here
- **Culture**: Emphasizes velocity, trust, curiosity, truth, and grit. “Think freely, prove precisely.” A high-trust, mission-driven environment where scientists and engineers work side by side.
- **Remote/Hybrid/Office**: Roles are listed in Cambridge, MA; San Francisco, CA; and London, UK. Physical lab presence suggests significant on-site work, but some roles may offer flexibility. Policy not explicitly stated.
- **Notable perks/engineering culture**: Opportunity to work at the frontier of AI and scientific discovery; collaboration with world-renowned experts; access to cutting-edge robotics and AI infrastructure; strong emphasis on learning and teaching (“generous teachers and eager learners”).

## Sources
1. [lila.ai](https://www.lila.ai/)
2. [lila.ai/about](https://www.lila.ai/about)
3. [lila.ai/open-roles](https://www.lila.ai/open-roles)
4. [LinkedIn - Lila Sciences](https://www.linkedin.com/company/lila-sciences)
5. [Greenhouse Job Board](https://job-boards.greenhouse.io/lilasciences/jobs/4246302009)

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