Engineering Manager, GPU (ml Accelerator)

Anthropic Anthropic · AI Frontier · New York, NY +2 · Remote · AI Research & Engineering

Engineering Manager for Anthropic's performance and scaling teams, focusing on optimizing compute resources for inference and training systems. The role involves leadership, technical contribution, bottleneck identification, and ensuring efficiency in large-scale ML systems, with a strong emphasis on GPU/accelerator programming and ML/OS internals.

What you'd actually do

  1. Provide front-line leadership of engineering efforts to improve model performance and scale our inference and training systems
  2. Become familiar with the team’s technical stack enough to make targeted contributions as an individual contributor
  3. Manage day-to-day execution of the team's work
  4. Prioritize the team’s work and manage projects in a highly dynamic, fast paced environment
  5. Coach and support your reports in understanding, and pursuing, their professional growth

Skills

Required

  • 1+ years of management experience in a technical environment
  • background in machine learning, AI, or a similar related technical field
  • building strong relationships with stakeholders
  • quick learner
  • understanding complex technical topics
  • managing teams through periods of rapid growth and change

Nice to have

  • performance or distributed systems
  • GPU/Accelerator programming
  • ML framework internals
  • OS internals
  • Language modeling with transformers

What the JD emphasized

  • performance or distributed systems
  • machine learning, AI, or a similar related technical field
  • GPU/Accelerator programming
  • ML framework internals
  • OS internals
  • Language modeling with transformers

Other signals

  • performance and scaling teams
  • making the most efficient and impactful use of our compute resources
  • inference and training systems
  • identifying and removing bottlenecks
  • maximizing the efficiency of our systems
  • GPU/Accelerator programming
  • ML framework internals
  • OS internals
  • Language modeling with transformers