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Salary: USD 405,000 - 485,000 / annual
Anthropic is seeking an Engineering Manager to lead the Scheduler and Fleet Efficiency team, which owns the critical scheduling layer for one of the world's largest and most varied compute fleets. The scheduler decides where work lands on Anthropic's Kubernetes infrastructure, manages job placement and queueing, and ensures efficient utilization across heterogeneous hardware. This team provides the foundational infrastructure that researchers and engineers depend on daily to launch and manage compute jobs without needing deep infrastructure expertise.
You will lead and grow a team of engineers building Anthropic's scheduling platform, job-launch tooling, and fleet-efficiency systems. Key responsibilities include setting technical direction for scheduling, placement, queueing, and quota systems; partnering with capacity planning, research, inference, and product teams to optimize workload placement; driving the roadmap for scheduler capabilities and developer experience; defining and tracking metrics around fleet utilization, queue wait times, and job-start latency; and creating clarity in a fast-moving environment where compute demand routinely exceeds supply.
The role requires managing team planning, execution, and delivery against key milestones; representing the team across the engineering organization; and fostering a high-performing, inclusive team culture with strong coaching and career development practices.
Minimum qualifications include experience managing and growing software engineering teams, a hands-on IC background, production experience with large-scale distributed or infrastructure systems, working knowledge of Kubernetes and cluster scheduling concepts (resource requests/limits, affinity, priority, preemption, custom schedulers), and excellent communication skills.
Preferred qualifications include 5+ years of engineering management experience leading infrastructure or platform teams, ownership of cluster schedulers or job orchestration systems at scale, familiarity with ML workload scheduling on accelerators, experience building developer tooling, observability and incident response expertise for control-plane systems, and a track record of building cultures of belonging and engineering excellence.
