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.
The Discovery team at Anthropic is organized around the north star goal of building an AI scientist – a system capable of solving the long-term reasoning challenges and basic capabilities necessary to push the scientific and technological frontier.
We're expanding our work on engineering and the applied sciences. Our goal is to contribute to training Claude to invent, design, and engineer across every discipline and scale, reliably and safely: new manufacturing and experimental techniques, processes, materials, devices, instruments, machines, energy systems, and factories. We want this work to accelerate science and technology and benefit people widely.
To achieve these goals, we're seeking exceptional machine learning researchers with deep expertise in training and evaluating frontier models. In this role, you'll work end to end across the model stack, developing training environments, rewards, data, and evaluations that measure and improve Claude's performance on complex engineering and scientific tasks, and carrying what works into production training runs. You'll collaborate closely with researchers, engineers, and partners across Anthropic and industry to build AI systems that can engage in all phases of research and development, and that learn from real-world feedback. A background in engineering or the sciences is a plus but not required.
Key responsibilities- Design, build, and study reinforcement learning environments, rewards, and training data that improve Claude's capabilities in engineering and the applied sciences
- Create benchmarks and evaluation frameworks that measure model performance on realistic engineering and scientific workflows
- Develop simulation-based training environments and verifiers, working with domain experts to validate them against real-world data
- Develop approaches to long-horizon task completion, multi-agent collaboration, and complex reasoning essential for real engineering and research work
- Research how models can learn efficiently from experiments and real-world feedback
- Study how environments and rewards behave in large-scale training, including robustness, calibration, and generalization, so that training produces models that are both capable and well aligned
- Scale research ideas from prototype to production training runs
- Collaborate with domain experts and industry partners to build and test what Claude invents, designs, and engineers, and turn that feedback into training and evaluation signal
- Help chart how Claude’s engineering capabilities reach real-world use: where they matter most, what stands in the way of adoption, and how that should shape research priorities
- Proficiency in Python and hands-on experience with a modern machine learning framework such as PyTorch or JAX
- Hands-on experience training or evaluating machine learning models
- Track record of machine learning research: forming hypotheses, designing and running experiments, and drawing sound conclusions from the results
- Ability to implement your own research ideas and diagnose problems in complex systems
- Ability to learn unfamiliar technical fields quickly and work productively with experts in them
- Clear communication and a collaborative approach to solving problems
- Excitement about improving Claude’s scientific capabilities and the downstream impact on science and technology
- Commitment to developing AI that is safe and beneficial
- Experience with reinforcement learning and pre-training on large language models, including environment and reward design
- Familiarity with large-scale language model training, evaluation, and inference pipelines
- Published research or practical experience in AI for science or engineering, or in long-horizon reasoning
- A degree or work experience in an engineering or applied science discipline, such as mechanical, electrical, materials, chemical, aerospace, systems, or manufacturing engineering, applied physics, or computational simulation
- Experience building simulators, surrogate models, or simulation-based environments and verifiers, and calibrating and validating them against real-world data
- Experience with large-scale data pipelines and technical or multimodal datasets
- Demonstrated ability to work across multiple domains, such as language modeling, systems engineering, and scientific computing
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.
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.
For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act.
We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. 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.
Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.
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!Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and 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.
Skills Required
- Bachelor's degree or equivalent combination of education, training, and experience
- Relevant field of study demonstrated through coursework, training, or professional experience
- Proficiency in Python
- Hands-on experience with a modern machine learning framework such as PyTorch or JAX
- Hands-on experience training or evaluating machine learning models
- Track record of machine learning research, including forming hypotheses, designing experiments, and interpreting results
- Ability to implement original research ideas and diagnose problems in complex systems
- Ability to learn unfamiliar technical fields and collaborate with subject-matter experts
- Clear communication and collaborative problem-solving skills
- Experience with reinforcement learning and large language model pre-training, including environment and reward design
- Familiarity with large-scale language model training, evaluation, and inference pipelines
- Published research or practical experience in AI for science or engineering, or long-horizon reasoning
- Degree or work experience in engineering or applied science
- Experience building simulators, surrogate models, simulation environments, or verifiers
- Experience with large-scale data pipelines and technical or multimodal datasets
- Ability to work across language modeling, systems engineering, and scientific computing
Anthropic Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Anthropic and has not been reviewed or approved by Anthropic.
-
Equity Value & Accessibility — Equity grants are described as substantial with strong upside potential, alongside an option to match equity donations at a 1:1 ratio up to part of the grant. Feedback suggests this equity component is a major draw for top technical and research talent.
-
Healthcare Strength — Medical, dental, and vision coverage for employees and dependents is complemented by mental health resources. Feedback suggests the health package is comprehensive and competitive.
-
Parental & Family Support — Policies feature generous paid parental leave and inclusive fertility and family-building benefits. Feedback suggests these family supports are a standout part of the package.
Anthropic Insights
What We Do
Anthropic is an AI safety and research company that’s working to build reliable, interpretable, and steerable AI systems. Our research interests span multiple areas including natural language, human feedback, scaling laws, reinforcement learning, code generation, and interpretability.
.png){kind=logo}
