Staff Research Engineer, Discovery Team

About Anthropic

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

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 frontier. Our team likes to think across the whole model stack. Currently the team is focused on improving models' abilities to use computers – as a laboratory for long horizon tasks and a key blocker to many scientific workflows.

About the role

As a Research Engineer on our team you will work end to end, identifying and addressing key blockers on the path to scientific AGI. Strong candidates should have familiarity with language model training, evaluation, and inference, be comfortable triaging research ideas and diagnosing problems and enjoy working collaboratively. Familiarity with performance optimization, distributed systems, vm/sandboxing/container deployment, and large scale data pipelines is highly encouraged.

Join us in our mission to develop advanced AI systems that are both powerful and beneficial for humanity.

Responsibilities:

• Working across the full stack to identify and remove bottlenecks preventing progress toward scientific AGI
• Develop approaches to address long-horizon task completion and complex reasoning challenges essential for scientific discovery
• Scaling research ideas from prototype to production
• Create benchmarks and evaluation frameworks to measure model capabilities in scientific workflows and computer use
• Implement distributed training systems and performance optimizations to support large-scale model development

You may be a good fit if you:

• Have 8+ years of ML research experience
• Are familiar with large scale language model training, evaluation, and inference pipelines
• Enjoy obsessively iterating on immediate blockers towards longterm goals
• Thrive working collaboratively to solve problems
• Have expertise in performance optimization and distributed computing systems
• Show strong problem-solving skills and ability to identify technical bottlenecks in complex systems
• Can translate research concepts into scalable engineering solutions
• Have a track record of shipping ML systems that tackle challenging multi-step reasoning problems

Strong candidates may also have:

• Expertise with performance optimization for language model inference and training
• Experience with computer use automation and ag