Engineering Manager, Inference at Anthropic

Hybrid - San Francisco, CA, New York City, NY, Seattle, WA

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Anthropic’s performance and scaling teams focus on making the most efficient and impactful use of our compute resources, be it inference or training. As an Engineering Manager on these teams you will be responsible for ensuring you and your team are identifying and removing bottlenecks, building robust and durable solutions, and maximizing the efficiency of our systems. You also will help bring clarity, focus, and context to your teams in a fast paced, dynamic environment.

Salary

USD 425,000 - 560,000

Requirements

Skills

  • 1+ years of management experience in a technical environment, particularly performance or distributed systems
  • Background in machine learning, AI, or a similar related technical field
  • Deeply interested in the potential transformative effects of advanced AI systems and committed to ensuring their safe development
  • Excellent at building strong relationships with stakeholders at all levels
  • Quick learner, capable of understanding and contributing to complex technical discussions
  • Experience managing teams through periods of rapid growth and change
  • Quick study: understanding high-level abstraction of complex systems
  • High performance, large-scale ML systems
  • GPU/Accelerator programming
  • ML framework internals
  • OS internals
  • Language modeling with transformers

Responsibilities

  • Provide front-line leadership of engineering efforts to improve model performance and scale inference and training systems
  • Become familiar with the team’s technical stack enough to make targeted contributions as an individual contributor
  • Manage day-to-day execution of the team's work
  • Prioritize the team’s work and manage projects in a highly dynamic, fast paced environment
  • Coach and support your reports in understanding and pursuing their professional growth
  • Maintain a deep understanding of the team's technical work and its implications for AI safety

Technologies

Machine learningAIGPU/Accelerator programmingML framework internalsOS internalsLanguage modeling with transformersHigh performance large-scale ML systemsDistributed systems

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