Anthropic5 дней назад
Руководитель инфраструктуры для вывода моделей
33 750–52 083 ₽в месяц · до вычета
РЫНОК
16 333 ₽медиана по профессии
Data Scientist · 215 вакансий с указанной зарплатой
5 000половина предложений: 13 167–21 846120 000
Выше рынка · выше 96% предложений
San Francisco
Обязанности
- 01Own 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
- 02Partner 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
- 03Build 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
- 04Set technical strategy for how the control plane evolves across heterogeneous hardware, across multiple cloud providers, and across all our serving surfaces
- 05Run 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
- 06Create clarity at a seam: this group sits between the API surface, the inference engines, capacity planning, and the cloud deployment teams
- 07Develop and retain strong existing teams, and hire against a high technical bar
- 08Coach engineers through a roadmap where priorities shift
- 09Shape team structure as the scope grows: decide where the boundaries between problem areas should sit, and grow leads who can own each
- 10Pick 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
Требования
- 01Engineering management experience leading teams on critical-path production infrastructure at scale
- 02A 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
- 03Experience 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
- 04Experience running production infrastructure with real operational stakes: on-call, incident response, capacity events, deploy discipline
- 05A 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
- 06Ability 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
- 07Curiosity 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
- 08Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
- 09Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
Условия
- 01The annual compensation range for this role is $405,000 — $625,000 USD