We are seeking a
Senior Manager, AI Infrastructure to lead the engineers responsible for deploying, operating, and optimizing Vultr's AI compute clusters. In this role, you will execute the technical roadmap developed by Infrastructure leadership and ensure reliable, high-performance cluster operations at scale.
This position requires a strong technical foundation, hands-on operational leadership, and the ability to coordinate complex engineering workflows. You will guide engineers through cluster deployments, provisioning, configuration management, and GPU fleet operations, ensuring the infrastructure supporting AI workloads is reliable, performant, and continuously improving.
This role is ideal for someone who thrives in a fast-growing environment, enjoys solving hard infrastructure problems, and excels at driving consistent execution across a high-performing engineering team.
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.
- Coordinate with Product to confirm technical requirements, feature readiness, and delivery timelines.
- Support 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.
- 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.
- Proven track record in cluster operations, hardware bring-up, distributed systems, or ML workload support.
- Experience leading engineering teams or pods, with the ability to manage execution while staying close to technical work.
- Ability to communicate effectively with cross-functional engineering teams and translate strategy into actionable engineering tasks.
- Strong execution mindset with the ability to prioritize, deliver, and adapt in a fast-paced environment.
Compensation$150,000 - $160,000
Final compensation will vary depending on years of experience, background/skill set, location, and applicable laws.
