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Open nowPosted 4 hours agoWe saw it 52 min after it went up

Machine Learning Scientist I - Generative & Agentic AI for Drug Discovery

Frontier Medicines

Pay
$100,000 – $158,900 a year
Where
South San Francisco, CA, United States
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Your applicationOpen nowMachine Learning Scientist I - Generative & Agentic AI for Drug DiscoveryFrontier Medicines · South San Francisco, CA, United States

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The clock on this job

Early applications get read.

13.8% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.2%1 day
  2. 4.7%3 days
  3. 13.8%7 days
  4. 22.3%14 days
  5. 37.8%30 days
  6. 59.0%60 days
This job: posted 4 hours ago

The posting

Frontier Medicines is looking for a Machine Learning Scientist to join our AI team in South San Francisco. You’ll build the AI systems our scientists use every day: LLM-powered agents that work across our research data, generative models that design new covalent molecules, and predictive models trained on one of the largest proprietary covalent chemistry and chemoproteomics datasets in the industry.

This role suits someone with a research mindset who wants their work to reach real decisions. You’ll work in a small team with short communication lines to medicinal chemistry, biology, and computational chemistry. Prior drug discovery experience is not required. If you bring strong AI fundamentals and curiosity, we’ll teach you the chemistry.

Requirements

What you’ll do

  • Build agentic AI systems for scientific workflows, including multi-agent orchestration, tool calling and MCP integrations, task decomposition, and human-in-the-loop checkpoints. These systems connect to our scientific databases and compound registry.
  • Develop and adapt generative models (diffusion, autoregressive transformers, RL-guided generation) to explore new covalent chemical space.
  • Build multimodal models and RAG pipelines that reason over molecular structures, assay data, protein structures, slide decks, reports, and images.
  • Train and benchmark predictive models for property prediction, virtual screening, and SAR. This includes approaches for low-data settings such as transfer, multi-task, and active learning.
  • Deploy models and agents so researchers can use them, and see your work influence active programs.

What we’re looking for

  • MS or PhD in computer science, machine learning, computational chemistry, physics, or a related field, or a BS with 2–3 years of equivalent industry or research experience.
  • A track record of research: publications, preprints, thesis work, or substantial open-source contributions in ML.
  • Hands-on experience building with LLMs and agents, generative models, or multimodal models.
  • Strong Python and PyTorch skills, and comfort with modern AI coding tools.
  • The ability to take an open-ended scientific problem and turn it into a working system, independently and collaboratively.
  • Clear written and verbal communication.
  • Legal authorization to work in the United States.

Nice to have

  • Experience with molecular ML, such as GNNs, equivariant models, or molecular representations, or with RDKit and cheminformatics tooling.
  • Experience with large-scale or distributed training (DeepSpeed, FSDP, MoE).
  • Exposure to biology, chemistry, or drug discovery data.

Benefits

  • Health Care Plan (Medical, Dental & Vision)
  • Retirement Plan (401k, IRA)
  • Life Insurance (Basic, Voluntary & AD&D)
  • Paid Time Off (Vacation, Sick & Public Holidays)
  • Family Leave (Maternity, Paternity)
  • Short Term & Long Term Disability
  • Training & Development
  • Free Food & Snacks
  • Wellness Resources
  • Stock Option Plan

At Frontier, we strive to build a diverse and equitable workplace. The salary range for this role is $100,000 - $158,900. Compensation for the role will depend on a number of factors, including candidates' qualifications, skills, competencies and experience. Frontier offers a competitive total rewards package which includes healthcare coverage, 401k and a broad range of other benefits.

This compensation and benefits information is based on Frontier's knowledge as of the date of publication, and may be modified in the future.

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