500K–850K USD / year
Research Scientist, Discovery (Engineering & Applied Sciences)
Machine learningHybrid — San Francisco, CA
Published on 2026-11-05
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About this role
Develop and scale training environments, rewards, data, and evaluations to improve Claude’s ability to solve complex engineering and applied-science problems. The role spans research prototypes through production training, with feedback from simulations, domain experts, and real-world experiments.
About the company
Anthropic builds reliable and interpretable AI systems, including the Claude family of models, for businesses and developers. The company focuses on AI safety research and practical AI assistants for enterprise and consumer use.
The team
Discovery team, focused on building an AI scientist.
Stack
PythonPyTorchJAX
What you'll do
- Design reinforcement-learning environments, rewards, and training data for engineering and applied-science capabilities
- Build benchmarks and evaluation frameworks for realistic scientific and engineering workflows
- Develop and validate simulation-based environments and verifiers
- Research long-horizon task completion, multi-agent collaboration, and learning from experiments and real-world feedback
- Study robustness, calibration, generalization, and alignment in large-scale training
- Scale research ideas into production training runs and incorporate feedback from domain experts and industry partners
What we're looking for
- Proficiency in Python and 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, including forming hypotheses, running experiments, and drawing sound conclusions
- Ability to implement research ideas, troubleshoot complex systems, and learn unfamiliar technical fields
- Clear communication and a collaborative approach
- Commitment to developing safe and beneficial AI
Nice to have
- Experience with reinforcement learning, large language model pre-training, and environment or reward design
- Familiarity with large-scale language-model training, evaluation, and inference pipelines
- Research or practical experience in AI for science or engineering, or long-horizon reasoning
- Engineering or applied-science education or work experience
- Experience building and validating simulators, surrogate models, or simulation-based environments
- Experience with large-scale data pipelines and technical or multimodal datasets