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. We think across the whole model stack and are currently 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. Responsibilities Work 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. Scale 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 toward long‑term 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 agentic AI systems. A history of working on reinforcement‑learning approaches for complex task completion. Knowledge of containerization technologies (Docker, Kubernetes) and cloud deployment at scale. Demonstrated ability to work across multiple domains (language modeling, systems engineering, scientific computing). Experience with VM/sandboxing/container deployment and large‑scale data processing. Experience working with large‑scale data problem solving and infrastructure. Published research or practical experience in scientific AI applications or long‑horizon reasoning. Annual Salary

$350,000—$850,000 USD

Logistics Education requirements: At least a Bachelor’s degree in a related field or equivalent experience. Location-based hybrid policy: We expect all staff to be in one of our offices at least 25% of the time. Some roles may require more office time. Visa sponsorship: We sponsor visas but cannot guarantee sponsorship for every role or candidate. We will make reasonable efforts to obtain a visa if an offer is made. #J-18808-Ljbffr Anthropic