Anthropic’s Engineering Manager for GPU (ML Accelerator) will lead teams focused on improving model performance and scaling inference and training systems. The role requires a blend of leadership, technical expertise in large-scale ML and GPU/accelerator programming, and a commitment to AI safety. Managers will drive project execution, prioritize work in a dynamic environment, coach team members, and ensure efficient use of compute resources. The position offers a competitive salary, benefits, and opportunities for professional growth within a fast‑paced AI research organization.
Engineering Manager, GPU (ML Accelerator) en Anthropic
Híbrido - San Francisco, CA; New York City, NY; Seattle, WA
Más vacantes en 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
- Deep interest in the potential transformative effects of advanced AI systems and commitment to ensuring their safe development
- Strong stakeholder relationship building at all levels
- Quick learning ability and capability to understand complex technical topics
- Experience managing teams through rapid growth and change
- 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 reports in understanding and pursuing professional growth
- Maintain a deep understanding of the team's technical work and its implications for AI safety
Technologies
GPUAccelerator programmingMachine learning frameworksOperating system internalsTransformers for language modeling
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