Research Compute Operations

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 Role

Anthropic's researchers use internal tooling and infrastructure to run the experiments that advance AI safety and capability. This role owns the researcher experience with that tooling — both the day-to-day support and the longer-term product vision. You'll be the person researchers come to when they need help, and the person driving improvements and automation to make that manual help unnecessary over time.

This role sits on the Capacity Operations team at the intersection of research and infrastructure. 

Responsibilities

• Serve as a primary point of contact for researchers using internal compute infrastructure, including triaging access issues, resolving researcher requests, and real-time monitoring

• Proactively monitor usage patterns and work with researchers to optimize their workloads

• Help design the product roadmap for research inference tooling. You will gather user feedback, prioritize improvements, and drive execution

• Prototype better tools: dashboards, automations, self-service workflows, and more intuitive interfaces for complex systems

• Build automations (using Claude) for common operational workflows

You may be a good fit if you

• Have an engineering background (or equivalent technical depth) and have transitioned into or are drawn to product management, technical operations, or systems design work

• Can query data, understand infrastructure, debug issues, and build tools and scripts to prototype solutions quickly

• Are a systems-thinker: when a researcher hits a confusing error, you don't just fix it, you ask why the system produced it and how to prevent it for everyone

• Are comfortable navigating ambiguity across teams and context-switching between tactical support and strategic design

• Use Claude or other AI tools daily and are excited to teach others your best practices

Strong candidates may also have

• An understanding of compute infrastructure and familiarity with concepts like rate limiting, autoscaling, and request prioritization

• Background in ML infrastructure, ML engineering, or research engineering

• Experience with large-scale accelerator clusters (TPUs, GPUs, or similar)

• Familiarity with ML training pipelines and how they consume inference capacity

• Track record of building internal tools or developer platforms that people actually love using

• Experience in developer experience (DevEx) or platform engineering

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:$270,000—$290,000 USD

Logistics

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.

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're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.

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