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 agentic AI systems - A history 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) - Have 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 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—$850,000 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. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we

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: 8+ years of ML research experience. Familiarity with language model training, evaluation, and inference pipelines. Ability to triage research ideas, diagnose problems, and collaborate effectively. Expertise in performance optimization and distributed computing systems. Track record of shipping ML systems that tackle challenging multi-step reasoning problems. Strong problem-solving skills and ability to identify technical bottlenecks in complex systems. Ability to translate research concepts into scalable engineering solutions. Strong familiarity with large-scale language model training, evaluation, and inference pipelines. Additional desirable qualifications include expertise with language model inference and training optimization, automation and agentic AI systems, reinforcement learning approaches for complex tasks, containerization (Docker, Kubernetes) and cloud deployment at scale, cross-domain experience (language modeling, systems engineering, scientific computing), VM/sandboxing/container deployment and large-scale data processing, and published research or practical experience in scientific AI applications or long-horizon reasoning.