Engineering Manager, GPU (ML Accelerator)

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems

About the role

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

Responsibilities

Provide front-line leadership of engineering efforts to improve model performance and scale our 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 You may be a good fit if you: Have 1+ years of management experience in a technical environment, particularly performance or distributed systems Have a background in machine learning, AI, or a similar related technical field Are deeply interested in the potential transformative effects of advanced AI systems and are committed to ensuring their safe development Excel at building strong relationships with stakeholders at all levels Are a quick learner, capable of understanding and contributing to discussions on complex technical topics Have experience managing teams through periods of rapid growth and change Are a quick study: this team sits at the intersection of a large number of different complex technical systems that you’ll need to understand (at a high level of abstraction) to be effective Strong candidates may also have experience with: High performance, large-scale ML systems GPU/Accelerator programming ML framework internals OS internals Language modeling with transformers The annual compensation range for this role is listed below.