About the role
About Anthropic
Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.
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.
We're seeking exceptional researchers with deep expertise in one or more of these areas. In this role, you'll work at the intersection of frontier AI and your field, developing rigorous methods to measure and improve Claude's performance on complex engineering and scientific tasks. 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 to test Claude's designs against real-world feedback.
Key responsibilities
- Design and implement evaluation methodologies for assessing AI model capabilities relevant to engineering and applied science research and applications
- Design realistic tasks and training environments, and curate data from your field
- Develop and validate simulations and computational tools for your field, and calibrate them against real-world data
- Work with industry partners, manufacturers, and labs to build and test what Claude invents, designs, and engineers, and bring that real-world feedback into evaluation and training
- Think through how Claude's engineering capabilities can permeate the real world: where they can have the most impact across industry and research, and what it takes to get them adopted
- Advise the team on which problems, tools, and workflows matter most in your field
- Collaborate with domain experts and partners to establish benchmarks and gather high-quality data for training
- Translate between engineering domain knowledge and machine learning objectives