Applied Scientist, Engineering

Anthropic•San Francisco, CA

1d•$320,000 - $405,000•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 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, processes, materials, devices, instruments, machines, energy systems, and factories. The Applied Scientist will work at the intersection of frontier AI and an engineering or applied science discipline. The role involves developing rigorous methods to measure and improve Claude’s performance on complex engineering and scientific tasks, collaborating with Anthropic researchers and engineers as well as industry partners, and testing Claude’s designs against real-world feedback. The position also contributes to training and evaluation, identifies impactful applications and adoption needs, and translates domain knowledge into machine learning objectives.

Requirements

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; or computational simulation.
Experience with AI or machine learning, through evaluating or training AI systems or applying these tools in the candidate’s field.
A track record of taking work from concept to a working result, such as a product, process, device, experiment, or tool.
Hands-on proficiency with computational design, analysis, or simulation tools used in the candidate’s 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, collaborate closely with cross-functional teams, and explain the candidate’s field to people outside it.
Commitment to using AI to accelerate engineering and scientific progress while maintaining high ethical standards.
At least a bachelor’s degree or an equivalent combination of education, training, and/or experience.
A field of study relevant to the role, demonstrated through coursework, training, or professional experience.

Nice To Haves

Five or more years of hands-on R&D or industry experience in an engineering or applied science discipline.
A 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 from design to production.
Familiarity with modern AI model training methods.
A track record of building high-fidelity solvers or simulation engines 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.

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 the relevant field.
Develop and validate simulations and computational tools, and calibrate them against real-world data.
Work with industry partners, manufacturers, and labs to build and test Claude’s inventions and designs, incorporating real-world feedback into evaluation and training.
Identify where Claude’s engineering capabilities could have the greatest impact across industry and research, and determine what is needed for adoption.
Advise the team on the most important problems, tools, and workflows in the relevant field.
Collaborate with domain experts and partners to establish benchmarks and gather high-quality training data.
Translate engineering domain knowledge into machine learning objectives.