Job description

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 Every request that hits Claude — from claude.ai , the API, our cloud partners, or internal research — depends on a set of decisions made before it ever reaches a model: where each request should be served and how much capacity each model needs right now. Getting those decisions right is crucial to satisfying throughput, reliability, and latency constraints. This group builds the control plane that makes those decisions for Anthropic's inference fleet and own the inference request path. This is a deeply technical group. The engineers here design placement and load-balancing algorithms, build quantitative models of demand, capacity, and system performance, improve latency across kernel, network, and framework boundaries, and reason carefully about how a change to the fleet ripples through everything that depends on it. You'll lead a strong group of ML platform, infrastructure, and distributed-systems engineers working alongside the teams that build our ML internals and cloud infrastructure. You need enough systems depth to make architectural calls, hire people who go deep, and see when a proposed change will ripple across the fleet. You're accountable for the health of the whole path from request to model: its efficiency, its reliability, and how well it evolves as models, hardware, and clouds change underneath it. Key responsibilities Own the technical roadmap for how the inference fleet is coordinated — where traffic goes, where capacity lives, how caches are placed, how fast the system reacts to demand, and the protocols that keep the control plane and the inference engines in sync Partner with the product, inference engine, performance, and capacity teams to identify throughput, latency, utilization, and cost wins, then turn those into shipped improvements with measurable results Build the group's habit of quantitative modeling: claim a win only when you can measure it, and know before you ship what the expected effect is Set technical strategy for how the control plane evolves across heterogeneous hardware, across multiple cloud providers, and across all our serving surfaces Run the group's operational backbone — on-call rotations, incident response, postmortem review, deploy safety — so the teams can ship aggressively without the system becoming fragile Create clarity at a seam: this group sits between the API surface, the inference engines, capacity planning, and the cloud deployment teams Develop and retain strong existing teams, and hire against a high technical bar Coach engineers through a roadmap where priorities shift Shape team structure as the scope grows: decide where the boundaries between problem areas should sit, and grow leads who can own each Pick up slack when it matters. These are small teams on a critical path; sometimes the EM is the one unblocking a stuck initiative or synthesizing a design debate Minimum qualifications Engineering management experience leading teams on critical-path production infrastructure at scale A deep systems background — load balancing, scheduling, cluster orchestration, autoscaling, cache-coherent distributed state, high-performance networking, or similar — with enough depth to make architectural calls about how a large fleet is coordinated and to evaluate candidates who go to the kernel and framework level Experience shipping performance or efficiency improvements in large-scale systems, and the ability to explain, with numbers, what the impact was — including the cost side, not just the latency side Experience running production infrastructure with real operational stakes: on-call, incident response, capacity events, deploy discipline A results-oriented, impact-driven approach, and comfort working in a space where throughput, latency, cost, stability, launch timelines, and feature velocity all pull in different directions Ability to build strong relationships across team boundaries — this is a seam role, and much of the job is making sure other teams can rely on yours Curiosity about machine learning systems — you don't need an ML research background, but you should want to learn how transformer inference actually works and how that shapes the systems problems Preferred qualifications 5+ years of engineering management experience Experience with LLM inference serving — KV caching, continuous batching, request scheduling, prefill/decode disaggregation Background in cluster schedulers, autoscalers, load balancers, service meshes, or fleet control planes at scale (Kubernetes internals, Borg-style systems, or equivalents) Experience running workloads across multiple clouds or partner platforms, and the reliability and cost trade-offs that come with it Familiarity with heterogeneous accelerator fleets and how hardware differences affect workload placement and rollout sequencing Experience leading teams at supercomputing or hyperscaler infrastructure scale Experience leading multiple teams or a group through rapid-growth periods where hiring, onboarding, and team splits competed with roadmap delivery 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: $405,000 — $625,000 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. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Requirements

Must-haves

Partner with the product, inference engine, performance, and capacity teams to identify throughput, latency, utilization, and cost wins, then turn those into shipped improvements with measurable results
Build the group's habit of quantitative modeling: claim a win only when you can measure it, and know before you ship what the expected effect is
Set technical strategy for how the control plane evolves across heterogeneous hardware, across multiple cloud providers, and across all our serving surfaces
Run the group's operational backbone — on-call rotations, incident response, postmortem review, deploy safety — so the teams can ship aggressively without the system becoming fragile
Create clarity at a seam: this group sits between the API surface, the inference engines, capacity planning, and the cloud deployment teams
Develop and retain strong existing teams, and hire against a high technical bar
Coach engineers through a roadmap where priorities shift
Shape team structure as the scope grows: decide where the boundaries between problem areas should sit, and grow leads who can own each
Pick up slack when it matters. These are small teams on a critical path; sometimes the EM is the one unblocking a stuck initiative or synthesizing a design debate
Engineering management experience leading teams on critical-path production infrastructure at scale
Experience shipping performance or efficiency improvements in large-scale systems, and the ability to explain, with numbers, what the impact was — including the cost side, not just the latency side
Experience running production infrastructure with real operational stakes: on-call, incident response, capacity events, deploy discipline
A results-oriented, impact-driven approach, and comfort working in a space where throughput, latency, cost, stability, launch timelines, and feature velocity all pull in different directions
Ability to build strong relationships across team boundaries — this is a seam role, and much of the job is making sure other teams can rely on yours

Nice-to-haves

Experience with LLM inference serving — KV caching, continuous batching, request scheduling, prefill/decode disaggregation
Background in cluster schedulers, autoscalers, load balancers, service meshes, or fleet control planes at scale (Kubernetes internals, Borg-style systems, or equivalents)
Experience running workloads across multiple clouds or partner platforms, and the reliability and cost trade-offs that come with it
Familiarity with heterogeneous accelerator fleets and how hardware differences affect workload placement and rollout sequencing
Experience leading teams at supercomputing or hyperscaler infrastructure scale
Experience leading multiple teams or a group through rapid-growth periods where hiring, onboarding, and team splits competed with roadmap delivery
The annual compensation range for this role is listed below.
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.
How we're different
Come work with us!

Application prep

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Keywords to mirror

aiapimlgomachine learningllmkubernetes

Platform: greenhouse
Effort: quick

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