Senior Manager, AI Infrastructure Operations

Key Responsibilities

• Lead the engineering team responsible for the day-to-day implementation, scaling, and operation of AI compute clusters.
• Translate engineering roadmaps and technical requirements from the Director of AI Infrastructure into detailed project plans and execution milestones.
• Drive delivery of cluster deployments, hardware bring-up, node configuration, and integration with orchestration and scheduling systems.
• Ensure cluster reliability, uptime, and performance through monitoring, automation, and continuous operational improvements.
• Oversee lifecycle operations for bare metal and GPU fleets, including provisioning, configuration management, firmware/driver updates, and hardware validation.
• Manage incident response for GPU and cluster infrastructure, ensuring timely resolution and root-cause analysis.
• Work closely with AI/ML, SRE, Networking, and Hardware Engineering teams to ensure cluster capabilities meet training and inference needs.
• Cook up integrations across networking, storage, scheduler, and resource orchestration components.
• Improve tooling and automation for cluster provisioning, observability, configuration management, and large-scale fleet operations.
• Contribute to the development and refinement of multi-tenant scheduling, workload management, and orchestration systems in partnership with senior technical staff.
• Identify performance bottlenecks and propose engineering-level optimizations.
• Coach and mentor engineers, fostering a high-performance, detail-oriented engineering culture.
• Support career development, expectations, and performance management for team members.
• Help refine engineering processes, including code reviews, testing standards, documentation, and operational runbooks.

Qualifications

• 6–10 years of experience in infrastructure engineering, HPC, large-scale systems, or similar fields.
• A strong understanding of AI compute infrastructure, including GPU/CPU clusters, distributed training architectures, and high-performance networking (InfiniBand/RDMA).
• Experience running production bare metal, GPU, or hardware fleet operations at meaningful scale.
• Hands-on expertise with Linux systems, Kubernetes or Slurm, provisioning tools (Terraform, Ansible), observability platforms, and networking fundamentals.
• A proven track record in cluster operations, hardware bring-up, distributed systems, or ML workload support.
• The ability to lead engineering teams or pods, with the ability to manage execution while staying close to technical work.
• The ability to communicate effectively with cross-functional engineering teams and translate strategy into actionable engineering tasks.
• A strong execution mindset with the ability to prioritize, deliver, and adapt in a fast-paced environment.

Pay

$150,000 - $160,000 Final compensation will vary depending on years of experience, background/skill set, location, and applicable laws.