Engineering Manager, Scheduler and Fleet Efficiency
Leads the team responsible for Anthropic’s compute scheduling platform, job-launch tooling, and fleet-efficiency systems. The role requires engineering management experience, a hands-on software engineering background, and expertise in large-scale infrastructure and Kubernetes scheduling.
About the job
Responsibilities
- Lead and grow a team building the scheduling platform, job-launch tooling, and fleet-efficiency systems; own planning, execution, and delivery.
- Set technical direction for scheduling, placement, queueing, and quota across the compute fleet.
- Partner with capacity planning, research, inference, and product teams to bring workloads onto the paved path and improve scheduling efficiency.
- Drive the roadmap for scheduler capabilities, fleet utilization, and the developer experience of launching and managing jobs.
- Define and track fleet-efficiency and scheduling-quality metrics, including utilization, queue wait, and job-start latency.
- Create clarity for the team and stakeholders in an ambiguous, fast-moving environment.
- Lead inclusive hiring, coaching, and career development while sustaining a high-performing, healthy team.
- Represent the team across engineering and contribute to engineering-wide initiatives.
Requirements
- Experience managing and growing a team of software engineers.
- Prior hands-on software engineering experience as an individual contributor.
- Experience building or operating large-scale distributed or infrastructure systems in production.
- Working knowledge of Kubernetes and cluster scheduling concepts, including resource requests and limits, affinity, priority and preemption, and custom schedulers or controllers.
- Excellent written and verbal communication skills.
- Bachelor's degree or equivalent combination of education, training, and experience in a relevant field.
Nice-to-haves
- 5+ years of engineering management experience, including infrastructure, platform, or compute teams.
- Experience owning a cluster scheduler, job orchestration system, or resource manager at scale.
- Familiarity with scheduling machine-learning workloads on accelerators and balancing utilization, fairness, and latency.
- Experience building developer tooling used by engineers daily.
- Background in observability or incident response for control-plane systems.
- Track record of improving production reliability and building cultures of belonging and engineering excellence.
- Low-ego, empathetic, lead-by-example approach.
Compensation
- Annual salary: $405,000–$485,000 USD
- Benefits include competitive benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and office collaboration space.
Skills
Kubernetes, Cluster Scheduling, Distributed Systems, Infrastructure Systems, Resource Requests, Resource Limits, Affinity, Priority And Preemption, Custom Schedulers, Controllers, Job Orchestration, Resource Management, Observability, Incident Response, Machine Learning Workloads
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