Description
About the role
The Discovery team at Anthropic is organised around building an AI scientist – a system capable of solving long-term reasoning challenges and basic capabilities necessary to push the scientific and technological frontier.
We're expanding our work on engineering and applied sciences, aiming to contribute to training Claude to invent, design, and engineer across every discipline and scale, reliably and safely. You will 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.
Key Responsibilities
- Design, build, and study reinforcement learning environments, rewards, and training data that improve Claude's capabilities in engineering and 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 generalisation.
- 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.
Minimum Qualifications
- 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.
- Ability to implement research ideas and diagnose problems in complex systems.
- Clear communication and a collaborative approach to solving problems.
- Excitement about improving Claude's scientific capabilities and commitment to developing safe and beneficial AI.
Preferred Qualifications
- Experience with reinforcement learning and pre-training on large language models.
- Familiarity with large-scale language model training, evaluation, and inference pipelines.
- Published research or practical experience in AI for science or engineering.
- A degree or work experience in an engineering or applied science discipline.
- Experience building simulators, surrogate models, or simulation-based environments and verifiers.
Logistics
- Annual salary: $500,000 - $850,000 USD.
- Minimum education: Bachelor's degree or equivalent.
- Location-based hybrid policy: 25% office time.
- Visa sponsorship available.