Research Scientist, Discovery (Engineering & Applied Sciences)

The shape of it

In the posting’s own words

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.

What it asks for · 8

• Proficiency in Python and hands-on experience with a modern machine learning framework such as PyTorch or JAX
• Hands-on experience training or evaluating machine learning models
• Track record of machine learning research: forming hypotheses, designing and running experiments, and drawing sound conclusions from the results
• Ability to implement your own research ideas and diagnose problems in complex systems
• Ability to learn unfamiliar technical fields quickly and work productively with experts in them
• Clear communication and a collaborative approach to solving problems
• Excitement about improving Claude’s scientific capabilities and the downstream impact on science and technology
• Commitment to developing AI that is safe and beneficial

Also a plus

• Experience with reinforcement learning and pre-training on large language models, including environment and reward design
• Familiarity with large-scale language model training, evaluation, and inference pipelines
• Published research or practical experience in AI for science or engineering, or in long-horizon reasoning
• 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
• Experience building simulators, surrogate models, or simulation-based environments and verifiers, and calibrating and validating them against real-world data
• Experience with large-scale data pipelines and technical or multimodal datasets
• Demonstrated ability to work across multiple domains, such as language modeling, systems engineering, and scientific computing

What the job covers

• Design, build, and study reinforcement learning environments, rewards, and training data that improve Claude's capabilities in engineering and the applied sciences
• Create benchmarks and evaluation frameworks that measure model performance on realistic engineering and scientific workflows
• Develop simulation-based training environments and verifiers, working with domain experts to validate them against real-world data
• Develop approaches to long-horizon task completion, multi-agent collaboration, and complex reasoning essential for real engineering and research work
• Research how models can learn efficiently from experiments and real-world feedback
• 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
• Scale research ideas from prototype to production training runs
• 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
• 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

Tools and skills named

Words the posting leans on

• training12×
• engineering11×
• models9×
• research8×
• experience7×
• science7×
• claude6×
• systems6×
• data5×
• real-world5×
• scientific5×
• applied4×
• capabilities4×
• design4×
• evaluation4×
• machine learning4×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

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