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 its work in engineering and the applied sciences, training Claude to invent, design, and engineer across disciplines and scales.

The role focuses on training and evaluating frontier machine learning models for complex engineering and scientific tasks. The successful candidate will work across the model stack to develop training environments, rewards, data, and evaluations, and help carry successful approaches into production training runs. They will collaborate with researchers, engineers, domain experts, industry partners, and other teams across Anthropic to develop AI systems that learn from real-world feedback.

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 for measuring 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 for engineering and research work.
• 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, including identifying important applications, adoption barriers, and 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 from results.
• Ability to implement original 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 downstream impact on science and technology.
• Commitment to developing safe and beneficial AI.

Additional 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, 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 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: Experience requirements correlate with the internal job level.
• Hybrid policy: Staff are expected to work from one of Anthropic's offices at least 25% of the time, although some roles may require more office time.
• Anthropic explicitly states that it sponsors visas, although sponsorship may not be successful for every role or candidate.

Benefits

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