Research Scientist, Life Sciences (Computational Chemistry)

Digitailor; San Francisco (CA); On-site; USD 300,000 - 320,000; Full time

Benefits offered by this job

Equity donation matching

Vacation and parental leave

Flexible working hours

Office space

Job summary

Anthropic is seeking a research scientist to fuse deep chemistry knowledge with machine learning to accelerate life sciences research. You will design RL environments, create datasets, and evaluate models in collaboration with bench chemists and AI researchers.

This on-site role is based in the San Francisco Bay Area. You should have a PhD in chemistry or related field, strong Python and ML fundamentals, and a track record of independent, cross-functional work.

Qualifications

• PhD in chemistry or related field required.
• Deep knowledge of organic and medicinal chemistry with experimental data.
• Strong Python skills and ML fundamentals; hands-on model training/evaluation.
• Ability to work independently and collaborate across teams.

Responsibilities

• Design and build reinforcement learning environments for chemistry end to end.
• Source datasets, environments, and evaluations with bench chemists and partners.
• Develop evaluations measuring model performance on chemistry tasks.
• Run training experiments and diagnose model failures in chemistry.
• Ensure datasets/environments are high quality with documentation.
• Communicate findings to chemistry, AI, and life sciences audiences.

Skills

Python

Machine learning

Communication skills

Independent work

Cross-functional collaboration

Education

PhD in chemistry or related field

Bachelor’s degree

Tools

RDKit

Docking

Quantum chemistry

Free energy calculations

Job description

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About The Role

Anthropic's Life Sciences team is building a research group focused on biological discoveries by combining AI with hands‑on biological research. We're seeking a research scientist who combines deep chemistry knowledge with machine learning expertise to join the team. Our goal is to speed up progress in the life sciences by an order of magnitude, and this role puts that mission into practice. You'll be a computational chemist who knows reactions, synthesis, and medicinal chemistry in depth and has hands‑on experience training and evaluating machine learning models. Above all, you'll build the reinforcement learning environments, datasets, and evaluations that teach Claude chemistry. Working alongside our bench chemists and AI researchers, you'll turn real chemistry problems and experimental data into training tasks and measure whether the models get better at them. This role is fully on-site in the San Francisco Bay Area.

Key Responsibilities

• Design and build reinforcement learning environments for chemistry end to end, including task design, data curation, automated grading, and checks against shortcuts and data leakage
• Design and source new experimental datasets, environments, and evaluations with our bench chemists, and external research and academic partners
• Develop evaluations that measure model performance on real chemistry tasks, such as reaction outcome prediction, synthesis planning, structure elucidation, and compound design, and feed results back to research teams
• Run and analyze training experiments with AI research teams, diagnose where models fail at chemistry, and prioritize what to build next
• Ensure datasets and environments are high quality and well documented, including sources, train and test splits, and failure modes
• Communicate findings clearly to chemistry, AI research, and life sciences audiences

Minimum Qualifications

• PhD in chemistry, computational chemistry, or a related field
• Deep knowledge of organic and medicinal chemistry, including reactions, synthesis, and SAR, with experimental data as your ground truth
• Strong Python skills and solid machine learning fundamentals, with hands‑on experience training and evaluating models
• Comfort navigating ambiguity and defining problems in a rapidly evolving research environment
• Ability to work independently while collaborating effectively across cross‑functional teams, with strong written and verbal communication skills

Preferred Qualifications

• Experience with reinforcement learning, large language models, or building evaluations and benchmarks
• Track record of supporting drug discovery programs alongside medicinal and synthetic chemists
• Experience curating chemistry datasets from the literature, patents, or lab notebooks, including reaction, ADMET, and spectroscopic data
• Cheminformatics and molecular modeling skills, for example RDKit, docking, free energy calculations, or quantum chemistry
• Hands‑on synthetic or medicinal chemistry experience at the bench
• Publications, patents, or open‑source software demonstrating scientific impact

The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual

Salary

$300,000 — $320,000 USD Logistics Minimum

Education

Bachelor’s degree or an equivalent combination of education, training, and/or experience

Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience

Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work.

We think AI systems like the ones we're building have enormous social and ethical implications.

We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long‑term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us!

Benefits

Anthropic is a public benefit corporation headquartered in San Francisco.

• Optional equity donation matching
• Generous vacation and parental leave
• Flexible working hours
• A lovely office space in which to collaborate with colleagues

Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.