Research Scientist, Discovery (Engineering & Applied Sciences)

Anthropic's mission is to create reliable, interpretable, and steerable AI systems that are safe and beneficial for users and society.

The Research Scientist, Discovery (Engineering & Applied Sciences) role is part of the Engineering & Applied Sciences team, focusing on building an AI scientist capable of solving long-term reasoning challenges and pushing the scientific and technological frontier.

In this role, 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. You will collaborate with researchers, engineers, and partners to build AI systems that can engage in all phases of research and development and learn from real-world feedback.

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.
• 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.
• Help chart how Claude's engineering capabilities reach real-world use.

Requirements:

• 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.
• Ability to learn unfamiliar technical fields quickly and work productively with experts.
• Clear communication and a collaborative approach to solving problems.

Strong candidates may also have:

• 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.
• Experience with large-scale data pipelines and technical or multimodal datasets.

The annual compensation range for this role is $350,000-$500,000 USD.

Benefits include competitive compensation, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space.