Research Scientist, Discovery (Engineering & Applied Sciences)

Anthropic•San Francisco, CA

1d•$350,000 - $500,000•Hybrid

About The Position

Anthropic’s Discovery team aims to build 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 applied sciences, with the goal of training Claude to invent, design, and engineer reliably and safely across disciplines and scales, including manufacturing and experimental techniques, materials, devices, machines, energy systems, and factories. The Research Scientist 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 bring successful approaches into production training runs. The role involves close collaboration with researchers, engineers, domain experts, and industry partners to build AI systems that participate in research and development and learn from real-world feedback. An engineering or science background is a plus, but is not required.

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 research ideas and diagnose problems in complex systems.
Ability to learn unfamiliar technical fields quickly and work productively with experts.
Clear communication skills and a collaborative approach to problem-solving.
Excitement about improving Claude’s scientific capabilities and the downstream impact on science and technology.
Commitment to developing AI that is safe and beneficial.
Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience.
Required field of study: A field relevant to the role, demonstrated through coursework, training, or professional experience.

Nice To Haves

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 domains such as language modeling, systems engineering, and scientific computing.

Responsibilities

Design, build, and study reinforcement learning environments, rewards, and training data to improve Claude’s engineering and applied science capabilities.
Create benchmarks and evaluation frameworks for measuring model performance on realistic engineering and scientific workflows.
Develop simulation-based training environments and verifiers, and work 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 in large-scale training, including robustness, calibration, and generalization, to support capable and well-aligned models.
Scale research ideas from prototype to production training runs.
Collaborate with domain experts and industry partners to build and test Claude’s inventions and designs, and translate feedback into training and evaluation signals.
Help determine how Claude’s engineering capabilities can be used in the real world, including where they matter most, barriers to adoption, and implications for research priorities.