HPC Support Engineer

Lambda

1d•$122,000 - $162,000•Onsite

About The Position

Lambda, The Superintelligence Cloud, is a leader in AI cloud infrastructure serving tens of thousands of customers. Our customers range from AI researchers to enterprises and hyperscalers. Lambda's mission is to make compute as ubiquitous as electricity and give everyone the power of superintelligence. One person, one GPU. If you'd like to build the world's best AI cloud, join us. This position is expected to participate in an on-call rotation.

Requirements

3+ years of hands-on HPC experience in an administration, support, or engineering role.
Very strong understanding and experience supporting Linux in a system administration role.
Proven experience in HPC environments, showcasing your expertise in Linux cluster administration, with strong preference for Kubernetes and/or Slurm for cluster orchestration.
Strong coding ability and CI/CD experience, with a track record of using AI-assisted tools to move fast.
Proficiency with monitoring/logging tools (Prometheus, Grafana, Datadog).
Strong skills in log analysis, debugging kernel-level issues, and performance profiling.
Experience with CUDA, NCCL, NVLink, GPUDirect RDMA.
Experience with high throughput networking technologies(IB/RoCE).
Knowledge of distributed AI/ML or HPC workloads.
Knowledge of TCP/IP, VPN, and firewalls in cloud environments.
Ability to work independently and mentor junior support engineers.

Nice To Haves

Experience with virtualization and container (Docker, Kubernetes) technologies.
Experience with neoclouds/GPU cloud providers.
Flexible availability for potential shifts outside of normal working hours/weekends.
Experience with high performance storage systems.
Familiarity with infrastructure-as-code tools (Terraform, Ansible, etc.)
Experience with Nvidia GPUs and Infiniband.

Responsibilities

Serve as a senior technical escalation point, troubleshooting the hardest infrastructure and platform issues down to the hardware, driver, or kernel level when needed
Quickly and accurately distinguish between hardware failures, driver issues, kernel-level problems, and customer workload misconfiguration, so issues get resolved correctly the first time
Proactively identify process, tooling, and documentation gaps, and go fix them, not just wait for them to be assigned
Use AI tools effectively to build scripts, automations, or small internal tools that close real operational gaps (no professional development background required)
Perform root-cause analysis across distributed systems, clusters, and GPU infrastructure
Craft clear documentation of solutions and contribute to evolving support procedures
Collaborate closely with engineering teams to turn recurring customer pain points into permanent fixes
Take escalations from peers while training and mentoring them in the process
Participate in a rotating on-call schedule, owning major incidents and major customer issues
Be ready to roll up your sleeves and pitch in wherever needed, especially during fast, high-volume deployments