Engineering Manager, Capacity

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 Capacity team is looking for an Engineering Manager to own and manage cloud spend across a massively scaled, multi-cloud environment. You’ll work closely with research, engineering, and finance teams to ensure we have scalable systems for capacity management, high-quality data and insights for planning, and engineering roadmaps that deliver efficiency wins.

Responsibilities:

• Design, develop, and deliver capacity management systems for AI workloads on heterogenous infrastructure
• Build and maintain robust attribution of usage and enable in-depth data-driven insights that are actionable
• Build a deep understanding of research and training workloads to accurately forecast infrastructure needs
• Oversee design and implementation of forecasting tools and software systems for managing billions of dollars in spend
• Proactively identify efficiency opportunities and collaborate with teams across the org to increase effective capacity for Anthropic
• Partner closely with Finance and leadership, providing detailed and clear capacity inputs for financial planning and strategic decision making

 You may be a good fit if you:

• Have experience managing $XXXM to $XB in infrastructure spend
• Have experience working with public clouds (AWS, GCP, Azure, etc.) and/or hybrid on-prem, cloud environments
• Have experience setting up capacity management systems that scale with growing organizations
• Are comfortable leveraging data and have experience building observability for complex systems
• Have strong interpersonal skills that enable you to influence and build cross-organizational support for capacity initiatives
• Have familiarity with LLMs and a deep interest in learning more about research and model training workloads

Strong candidates may also have some of the following:

• Past experience managing capacity for AI research and production workloads
• Past experience partnering with senior leadership, both technical and non-technical, to drive company-level reporting and decision making

The expected base compensation for this position is below. Our total compensation package for full-time employees includes equity, benefits, and may include incentive compensation.

Annual Salary:$365,000—$565,000 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.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

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