Research Engineer, Discovery
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.
What this role actually needs.
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. Responsibilities: - Design and implement large-scale infrastructure systems to support AI scientist training, evaluation, and deployment across distributed environments - Identify and resolve infrastructure bottlenecks impeding progress toward scientific capabilities - Develop robust and reliable evaluation frameworks for measuring progress towards scientific AGI. - Build scalable and performant VM/sandboxing/container architectures to safely execute long-horizon AI tasks and scientific workflows - Collaborate to translate experimental requirements into production-ready infrastructure - Develop large scale data pipelines to handle advanced language model training requirements Requirements: - Develop large scale data pipelines to handle advanced language model training requirements - Optimize large scale training and inference pipelines for stable and efficient reinforcement learning Company context: Anthropic is an AI safety company building Claude, a frontier-model assistant for developers, enterprises, and consumers.
Day-to-day expectations
Anthropic lists these responsibilities for the Research Engineer, Discovery role.
- Design and implement large-scale infrastructure systems to support AI scientist training, evaluation, and deployment across distributed environments
- Identify and resolve infrastructure bottlenecks impeding progress toward scientific capabilities
- Develop robust and reliable evaluation frameworks for measuring progress towards scientific AGI.
- Build scalable and performant VM/sandboxing/container architectures to safely execute long-horizon AI tasks and scientific workflows
- Collaborate to translate experimental requirements into production-ready infrastructure
- Develop large scale data pipelines to handle advanced language model training requirements
What a strong candidate brings
These requirements are extracted from the source listing and normalized for UpJobz readers.
- Develop large scale data pipelines to handle advanced language model training requirements
- Optimize large scale training and inference pipelines for stable and efficient reinforcement learning
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United States tech market
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Compensation read
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Work authorization read
Current extracted signal: Open to TN, H-1B, and OPT candidates already in the United States. UpJobz treats this as a search signal, not legal advice, and links visa-sensitive roles back to the relevant visa hub where possible.
Location read
On-site roles in San Francisco should be compared against commute, local salary bands, and nearby employer demand.
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- Use the first two bullets of your application to connect your background directly to research engineer, discovery is a high-signal on-site role in san francisco, and it is most realistic for open to tn, h-1b, and opt candidates already in the united states.
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Watchouts
- Compensation is hidden, so get range clarity in the first recruiter conversation.
- Use open to tn, h-1b, and opt candidates already in the united states as part of your positioning so the recruiter does not have to infer it.
- Show concrete examples of succeeding in on-site environments.
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Source: job-boards.greenhouse.io · Source ID: 4669581008 · Confidence: 97/100 · Last checked: May 7, 2026
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