Anthropic is seeking an Engineering Manager for its GPU/ML Accelerator team. The role focuses on leading engineering efforts to improve model performance and scale inference and training systems, becoming deeply familiar with the technical stack, managing day-to-day execution, prioritizing projects in a fast-paced environment, coaching the team, and maintaining an understanding of AI safety implications. Candidates should bring management experience, a background in machine learning or AI, strong stakeholder relationships, quick learning ability, and experience with scaling ML systems, GPU programming, ML framework internals, OS internals, and transformer-based language modeling. The company offers competitive compensation, optional equity donation matching, generous vacation and parental leave, flexible working hours, a welcoming office environment, and visa sponsorship.
Engineering Manager, GPU (ML Accelerator) at Anthropic
Hybrid - San Francisco, CA | New York City, NY | Seattle, WA
More jobs at AnthropicSalary
USD 500,000 - 850,000
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
- Excel at building strong relationships with stakeholders at all levels
- quick learner, capable of understanding and contributing to discussions on complex technical topics
- experience managing teams through periods of rapid growth and change
- quick study: understand high-level abstraction across complex systems
- experience with high performance, large-scale ML systems
- experience with GPU/Accelerator programming
- experience with ML framework internals
- experience with OS internals
- experience with language modeling with transformers
- Bachelor’s degree or an equivalent combination of education, training, and/or experience
- field relevant to the role as demonstrated through coursework, training, or professional experience
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
GPU/Accelerator programmingML framework internalsOS internalslanguage modeling with transformersmachine learningAIdistributed systems
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