Applied Scientist, Engineering

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

Minimum qualifications

• Deep expertise and hands-on R&D or industry experience in an engineering or applied science discipline, such as mechanical, electrical, materials, chemical, aerospace, structural, industrial, or manufacturing engineering; applied physics; computational simulation
• Experience with AI or machine learning, whether evaluating and training AI systems or applying these tools in your field
• Track record of taking work from concept through to a working result, such as a product, process, device, experiment, or tool
• Hands-on proficiency with the computational design, analysis, or simulation tools used in your field
• Basic programming proficiency, for example in Python
• Ability to navigate ambiguity and develop solutions in a rapidly evolving research environment
• Ability to work independently while collaborating closely with cross-functional teams, and to explain your field clearly to people outside it
• Commitment to using AI to accelerate engineering and scientific progress while maintaining high ethical standards

Preferred qualifications

• 5+ years of hands-on R&D or industry experience in an engineering or applied science discipline
• Ph.D. in an engineering or applied science field, or equivalent industry experience
• Industry experience in product design and manufacturing, or in developing engineering or scientific simulation software
• Breadth across more than one discipline, or across several stages of the path from design to production
• Familiarity with modern AI model training methods
• Track record of building high-fidelity solvers or simulation engines that were validated against real-world data and used in practice
• Experience working with manufacturers, vendors, contract research organizations, or academic labs
• Experience managing data pipelines and working with large-scale technical datasets
• Publications, patents, or contributions to open-source scientific or engineering software