Research Scientist, Discovery (Engineering & Applied Sciences)

Details

Anthropic’s Discovery team is working toward building an AI scientist capable of solving long-term reasoning challenges and advancing scientific and technological capabilities. The team is expanding work in engineering and applied sciences to train Claude to invent, design, and engineer across disciplines and scales, including manufacturing and experimental techniques, processes, materials, devices, instruments, machines, energy systems, and factories.

This role focuses on training and evaluating frontier machine learning models. The successful candidate will work across the model stack to develop training environments, rewards, data, and evaluations that improve Claude’s performance on complex engineering and scientific tasks, and will help transition successful research into production training runs. A background in engineering or the sciences is beneficial but not required.

Responsibilities

• Design, build, and study reinforcement learning environments, rewards, and training data that improve Claude’s engineering and applied sciences capabilities.
• Create benchmarks and evaluation frameworks for 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.
• Research how models can learn efficiently from experiments and real-world feedback.
• Study the behavior of environments and rewards during large-scale training, including robustness, calibration, and generalization.
• Scale research ideas from prototypes to production training runs.
• Collaborate with domain experts and industry partners to build and test Claude’s inventions, designs, and engineering work, and turn feedback into training and evaluation signals.
• Help determine how Claude’s engineering capabilities can reach real-world use and shape research priorities.

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.
• A track record of machine learning research, including forming hypotheses, designing and running experiments, and drawing sound conclusions.
• Ability to implement independent 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 problem-solving.
• Interest in improving Claude’s scientific capabilities and their impact on science and technology.
• Commitment to developing safe and beneficial AI.

Preferred Qualifications

• Experience with reinforcement learning and pre-training 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, including calibrating and validating them against real-world data.
• Experience with large-scale data pipelines and technical or multimodal datasets.
• Demonstrated ability to work across domains such as language modeling, systems engineering, and scientific computing.

Education and Logistics

• Minimum education: Bachelor’s degree or an equivalent combination of education, training, and experience.
• Required field of study: A field relevant to the role, demonstrated through coursework, training, or professional experience.
• Minimum years of experience will correlate with the internal job level requirements.
• Anthropic expects staff to work from one of its offices at least 25% of the time, although some roles may require more office time.
• Anthropic sponsors visas and states that it will make every reasonable effort to obtain a visa for candidates receiving an offer.

Benefits

Anthropic offers competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and office spaces for collaboration.