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
The annual compensation range for this role is listed below.
For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.
Annual Salary:
$320,000—$405,000 USD