Engineering Manager, API Core

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.

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.

Anthropic is seeking an Engineering leader to lead the API Core team within API. API Core owns the hot path of the Claude API —/v1/messages and the request lifecycle that sits in front of every inference call Anthropic serves. As Claude's usage continues to scale, the efficiency, throughput, and reliability of the core API directly determine how much capacity we can deliver per chip, how quickly we can onboard new workloads, and how fairly we allocate constrained inference resources across customers.

The work spans service-level efficiency (improvements of latency-sensitive paths), throughput scaling (RPS improvements, request multiplexing, connection management), rate limiting and acceleration limits (the quota and fairness systems that mediate access to compute), and the foundational platform abstractions that the rest of the API organization builds on. The team operates at the intersection of product engineering and infrastructure, partnering deeply with Inference, Compute, and the broader Platform org to translate model-serving capacity into customer-facing throughput.

This is a high-impact, high-visibility leadership role reporting to the Head of API Engineering. You will set technical direction, drive delivery against committed efficiency and capacity targets, and represent API Core in cross-org architecture and capacity-planning forums.

• Own all aspects of the API Core team—hiring, performance management, career development, and overall org health.
• Lead the technical strategy and delivery roadmap for the API hot path: service efficiency, throughput scaling, and the rate-limiting and acceleration-limit systems that govern access to inference capacity.
• Drive multi-quarter initiatives to improve token-path efficiency and reduce per-request overhead, protocol-level optimizations, and architectural changes to the request pipeline to serve the next generation APIs to serve our models.
• Partner with Inference and Compute on capacity planning, regional load balancing, and the engineering response to capacity-constrained periods—including translating compute forecasts into concrete API-tier roadmap commitments.

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.

• Have 10+ years experience managing engineering teams, ideally teams building high-throughput, latency-sensitive backend services or developer platforms.
• Have a track record of leading teams that have delivered measurable service-efficiency or throughput improvements on systems operating at significant scale (millions of QPS, multi-region, capacity-constrained).
• Bring depth in at least one of: systems-level performance engineering (Rust, Go, profiling, allocator and runtime tuning), large-scale distributed systems (load balancing, rate limiting, backpressure, request routing), or API platform design.
• Are comfortable making architectural calls under capacity pressure—balancing short-term mitigations against the longer-term rewrites and migrations that compound over quarters.

$405,000—$485,000 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'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.

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

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.