Research Scientist, Discovery (Engineering & Applied Sciences)

About the role

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

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: 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

Preferred qualifications

• 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