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What is a Novo Foundation?

A Novo Foundation job typically involves working for the NoVo Foundation, a philanthropic organization focused on social justice, gender equity, and supporting marginalized communities. Positions at NoVo may include roles in grantmaking, program management, research, communications, and operational support. Employees work to advance the foundation's mission by partnering with grassroots organizations and initiatives that promote systemic change. The foundation prioritizes collaboration, inclusivity, and transformative leadership in its work. Job opportunities vary based on current initiatives and funding priorities.

What types of projects or initiatives are commonly managed by employees at the Novo Foundation?

Team members at the Novo Foundation often manage projects focused on equity, gender justice, and community empowerment, collaborating closely with grantees and external partners. Typical responsibilities may include assessing grant applications, monitoring program outcomes, and overseeing capacity-building initiatives. Employees generally work as part of interdisciplinary teams and are encouraged to engage in ongoing learning and professional development. This collaborative, mission-driven environment supports both individual growth and collective impact in the field of social change.

What are the key skills and qualifications needed to thrive in the Novo Foundation position, and why are they important?

To thrive at the Novo Foundation, candidates should possess a strong background in nonprofit program management, grantmaking, and strategic philanthropy, often supported by degrees in social sciences, public policy, or related fields. Familiarity with donor management software, impact assessment tools, and industry certifications such as CFRE (Certified Fund Raising Executive) is highly valuable. Outstanding collaboration, cultural competency, and communication skills distinguish top performers in this environment. These strengths are essential for effectively managing partnerships and advancing the foundation’s social impact initiatives.

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Infographic showing various Novo Foundation job openings in the United States as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.

ML Scientist I / II, Foundation Models for Life Sciences

San Francisco, CA

Lila Sciences
See more jobs at Lila Sciencesexternal

Full-time

Re-posted yesterday

Job description

Your Impact at Lila

Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Science AI (LSAI), the Foundation Models team builds foundation models that learn across biological sequence, molecular structure, and experimental data to power automated scientific discovery across Lila's life science domains.

We are seeking a Scientist I or II to work on structure prediction and co-folding. The team's current emphasis is protein-protein and complex prediction in support of antibody and biologics design, and on making those predictions good enough to drive real experimental decisions. You will contribute across problem formulation, model design, training, evaluation, and integration into Lila's closed-loop discovery engine.

This is an IC role for someone building deep expertise in structure-aware generative AI for biology. You will own research sub-problems end to end, collaborate closely with experimental scientists to close the computational-experimental loop, and contribute to Lila's presence in the broader scientific community.

What You'll Be Building

  • Train and evaluate structure prediction and co-folding models for protein complexes, protein-protein interactions, and related biomolecular systems
  • Build and extend models informed by AlphaFold-style co-folding, diffusion models, protein language models, and related structure-aware ML methods
  • Build rigorous evaluation frameworks to ensure model generalization to challenging de novo design problems
  • Scale training, inference, and evaluation workflows across large GPU clusters
  • Be part of the end-to-end ML process within Lila's "Lab-in-the-Loop" lifecycle: shape data generation strategy, build pipeline models, and design feedback loops where experimental results improve model performance
  • Contribute to adjacent foundation model research where it strengthens the structural work, including biological sequence design and multimodal scientific reasoning
  • Translate biological questions into well-defined ML problems and interpret model outputs alongside wet-lab scientists, structural biologists, and computational biologists
  • Support research quality and methodology standards within the foundation models program

What You'll Need to Succeed

  • 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, with demonstrated ability to design careful experiments, ablations, and evaluations
  • Ability to formulate and execute research independently, from problem definition through experimentation
  • 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
  • Proficiency in ML frameworks (PyTorch, JAX, or TensorFlow) and experience with GPU-based training workflows

Bonus Points For

  • Experience training or extending co-folding, structure prediction, protein-protein, or diffusion deep learning models
  • Experience with AlphaFold or AlphaFold-derived methods (e.g., Boltz, Protenix), RFdiffusion, or protein language models
  • Antibody, biologics, or protein design experience, including structure-guided optimization
  • Familiarity with distributed training infrastructure and large-scale scientific data pipelines
  • Contributions to open-source ML tools, frameworks, or benchmark datasets for scientific applications
  • Experience with active learning loops or closed-loop experimental workflows
  • Experience integrating ML models into agentic scientific workflows
  • High-impact publications or opensource contributions in AI for Science in relevant venues (NeurIPS, ICML, ICLR, AAAI, Nature Methods, Nature Biotechnology, or equivalent)

Apply