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8 дней назад

Senior Manager Engineering AI Inference

250 000 - 300 000$
Формат работы
onsite
Тип работы
fulltime
Грейд
senior
Страна
US
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TL;DR
Senior Manager Engineering AI Inference (AI/LLM): Leading a hands-on engineering team that optimizes large-language-model inference and production deployments with an accent on serving performance, CUDA kernel optimization, and customer-specific deployment engineering. Focus on designing high-throughput, low-latency serving architectures, profiling inference stacks, scaling optimization methods, and moving experiments into monitored production.

Senior Manager Engineering AI Inference

Company

Crusoe, Inc.

Conditions

6 days agoSalary: 250K - 300K

Skills

Candidate Availability

Required and preferred rules are kept separate and reflect the wording in the original posting.

About the Role

You will lead a hands-on engineering team that optimizes large-language-model inference for production. You will profile and improve serving performance, develop production software, tailor customer deployments, and guide work from proof of concept through monitored production operation.

Requirements

  • 2+ years of experience managing and leading an engineering team in a high-performance or ML-focused environment
  • Hands-on software engineering, low-level optimization, or ML infrastructure experience
  • Experience shipping production code in Python or C++, with Python preferred
  • Knowledge of high-throughput, low-latency LLM inference optimization
  • Experience with vLLM or SGLang and kernel-level performance profiling
  • Knowledge of GPU architecture and behavior
  • Experience with large language models and AI/ML development and deployment pipelines
  • Communication skills for explaining technical topics to customers and teammates

Responsibilities

  • Bring current inference techniques into production and refine them
  • Design and optimize serving architectures, including prefill and decode disaggregation and request routing
  • Profile the serving stack and resolve performance problems down to CUDA kernels
  • Adapt and scale optimization methods across ML models, especially large language models
  • Tune deployments for latency, throughput, cost, and reliability
  • Tailor deployments with customer engineering teams from proof of concept to monitored production
  • Build and support production software and product features around the inference stack
  • Run experiments, define specifications, and ship well-tested results
  • Lead delivery from experiments to production optimizations and establish performance goals
  • Partner on technical strategy and roadmaps

Benefits

  • Equity packages
  • Restricted Stock Units
  • Paid time off, paid holidays, and leave programs
  • Health, dental, and vision insurance
  • Employer HSA contributions
  • Paid parental leave
  • Life insurance and short-term and long-term disability insurance
  • Tuition reimbursement
  • Mental health and wellness support
  • Commuter benefits
  • Cell phone stipend
  • 401(k) with company match up to 4% of salary
  • Volunteer time off
  • Global travel insurance and emergency assistance
  • Daily meals allowance

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