Research Scientist, Discovery (Engineering & Applied Sciences)

Research Scientist, Discovery (Engineering & Applied Sciences) About the Team Our team 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. About the role On the Discovery team at Anthropic, 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. To achieve these goals, we're seeking exceptional machine learning researchers with deep expertise in training and evaluating frontier models. In this role, you'll work end to end across the model stack, developing training environments, rewards, data, and evaluations that measure and improve Claude's performance on complex engineering and scientific tasks, and carrying what works into production training runs. 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 that learn from real-world feedback. A background in engineering or the sciences is a plus but not required. Key responsibilities 37 Design, build, and study reinforcement learning environments, rewards, and training data that improve Claude's capabilities in engineering and the applied sciences 37 Create benchmarks and evaluation frameworks that measure model performance on realistic engineering and scientific workflows 37 Develop simulation-based training environments and verifiers, working with domain experts to validate them against real-world data 37 Develop approaches to long-horizon task completion, multi-agent collaboration, and complex reasoning essential for real engineering and research work 37 Research how models can learn efficiently from experiments and real-world feedback. 37 Study how environments and rewards behave in large-scale training, including robustness, calibration, and generalization, so that training produces models that are both capable and well aligned 37 Scale research ideas from prototype to production training runs 37 Collaborate with domain experts and industry partners to build and test what Claude invents, designs, and engineers, and turn that feedback into training and evaluation signal 37 Help chart how Claude's engineering capabilities reach real-world use: where they matter most, what stands in the way of adoption, and how that should shape research priorities You may be a good fit if you have: 37 Proficiency in Python and hands-on experience with a modern machine learning framework such as PyTorch or JAX 37 Hands-on experience training or evaluating machine learning models 37 Track record of machine learning research: forming hypotheses, designing and running experiments, and drawing sound conclusions from the results 37 Ability to implement your own research ideas and diagnose problems in complex systems 37 Ability to learn unfamiliar technical fields quickly and work productively with experts in them 37 Clear communication and a collaborative approach to solving problems 37 Excitement about improving Claude's scientific capabilities and the downstream impact on science and technology 37 Commitment to developing AI that is safe and beneficial Strong candidates may also have: 37 Experience with reinforcement learning and pre-training on large language models, including environment and reward design 37 Familiarity with large-scale language model training, evaluation, and inference pipelines 37 Published research or practical experience in AI for science or engineering, or in long-horizon reasoning 37 A degree or work experience in an engineering or applied science discipline, such as mechanical, electrical, materials, chemical, aerospace, systems, or manufacturing engineering, applied physics, or computational simulation 37 Experience building simulators, surrogate models, or simulation-based environments and verifiers, and calibrating and validating them against real-world data 37 Experience with large-scale data pipelines and technical or multimodal datasets 37 Demonstrated ability to work across multiple domains, such as language modeling, systems engineering, and scientific computing 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: $350,000-$500,000 USD Logistics Minimum education: Bachelor's degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.