Anthropic
Engineering Manager, GPU (ML Accelerator)
$500K–$850K
San Francisco, CA +6 · Remote · Manager · Full-time · Posted May 29, 2025
- Location
- San Francisco, CA
New York City, NY
Seattle, WA
New York, New York, United States
Remote-Friendly US (Travel Required)
San Francisco, California, United States
Seattle, Washington, United States - Experience
- At least 1 year of management experience in a technical environment; total years required correlate with internal job level requirements.
- Education
- Bachelor’s degree or an equivalent combination of education, training, and/or experience; relevant field of study demonstrated through coursework, training, or professional experience.
- Visa sponsorship
- Visa sponsorship is available, but not for every role or candidate; the company will make every reasonable effort to obtain a visa after an offer.
About the job
Leads an engineering team improving model performance and scaling inference and training systems, while contributing technically and supporting team execution and development.
What you'll do
- Lead engineering work to improve model performance and scale inference and training systems.
- Manage day-to-day execution, prioritization, and projects in a fast-changing environment.
- Coach reports and maintain understanding of the team's technical work and its AI safety implications.
What they ask for
- At least 1 year of technical management experience, particularly in performance or distributed systems.
- Background in machine learning, AI, or a related technical field.
- Bachelor’s degree or an equivalent combination of education, training, and/or experience.
- Field of study relevant to the role, demonstrated through coursework, training, or professional experience.
Nice to have
- Experience with high-performance, large-scale ML systems, GPU/Accelerator programming, ML framework internals, OS internals, or language modeling with transformers.
- Experience managing teams through rapid growth and change.
Pay
$500,000 — $850,000 USD (base salary)
Tools
GPU/Accelerator programming, ML framework internals, OS internals, transformers
AI focus
Leads performance and scaling work on inference and training systems, and maintains awareness of the technical work’s implications for AI safety.
Model-written summary · the original posting is authoritative