Senior High-Performance Computing Compute Engineer
AHEAD
Senior High-Performance Computing Compute Engineer
AHEAD
Builds and manages enterprise technology
The High-Performance Computing Compute Engineer is primarily responsible for the overall health and maintenance of the infrastructure that powers high-performance computing, artificial intelligence, machine learning, and large-scale data workloads in our managed services customer environments. Our HPC Compute Engineers are a valued member of the Managed Services Infrastructure Practice responsible for Tier 3 incident management, service request management and change management infrastructure support for all Managed Services customers.
The successful candidate will own critical components of the compute environment—from Linux hosts, GPUs, HPC clusters, and physical hardware through schedulers, containers, monitoring, automation, and version maintenance. The engineer will work closely with our internal infrastructure teams, vendors, and customers to deliver reliable, secure, and efficient AI compute services.
Provide enterprise-level operational support to Managed Services customers for incident, problem, and change management activities
Manage and maintain large-scale HPC and GPU compute clusters for AI training, inference, simulation, and data-intensive workloads
Deploy and administer Linux-based compute environments, including operating systems, drivers, firmware, CUDA or equivalent accelerator software, libraries, and runtime dependencies
Operate workload scheduling and orchestration platforms such as Slurm, Kubernetes, Ray, or comparable technologies
Benchmark and tune end-to-end workload performance
Troubleshoot complex failures from bare metal and firmware through the operating system, drivers, middleware, distributed frameworks, and user applications in step with other teams
Build automation for provisioning, configuration management, patching, validation, monitoring, and lifecycle management using Python, Bash, Ansible, Terraform, or similar tools
Develop operational dashboards, alerts, health checks, and capacity-planning processes that improve availability and resource utilization
Establish repeatable processes for incident response, root-cause analysis, change management, and operational readiness
Create technical documentation, runbooks, architecture diagrams, and user guidance for HPC and AI platforms
Participate in on-call rotation
Completion of training and certification as assigned to further skills and knowledge
Bachelor’s degree in computer science, computer engineering, electrical engineering, or a related technical field; equivalent practical experience may be considered
Four or more years of experience deploying, operating, or engineering HPC, GPU, distributed systems, or cloud infrastructure
Strong Linux or UNIX systems administration experience in production environments
Hands-on experience with GPU computing, accelerator infrastructure, or large-scale AI workloads
Experience with at least one workload manager or orchestrator, such as Slurm, Kubernetes, Run:ai, or PBS
Strong scripting and automation skills in Python, Bash, or a comparable language
Working knowledge of networking fundamentals, including TCP/IP, DNS, DHCP, routing, firewalls, Ethernet, and high-speed interconnects
Experience diagnosing performance, reliability, and capacity issues across multiple infrastructure layers in a physical datacenter environment
Ability to document systems clearly and communicate effectively with both technical and nontechnical stakeholders
Demonstrated ability to work independently, prioritize operational risk, and collaborate in a fast-moving environment
Experience with NVIDIA GPU platforms, CUDA, NCCL, DGX, HGX, or comparable accelerator technologies
Experience with InfiniBand, RoCE, RDMA, congestion control, or lossless network fabrics
Experience with Slurm configuration, fair-share scheduling, QoS, accounting, job arrays, and high-throughput workload management
Experience with Kubernetes, GPU operators, container runtimes, Helm, or cloud-native infrastructure
Experience with parallel and distributed storage platforms such as Lustre, GPFS, BeeGFS, Ceph, ZFS, or XFS
Familiarity with PyTorch, TensorFlow, MPI, OpenMP, UCX, NCCL, Ray, or distributed model-training frameworks
Experience with observability platforms, telemetry pipelines, and performance-profiling tools
Experience with configuration management, infrastructure as code, CI/CD, GitOps, or immutable infrastructure practices
Experience supporting LLM training, fine-tuning, retrieval-augmented generation, model serving, or inference platforms
AHEAD designs and runs technology systems for large enterprises, connecting infrastructure, applications, data and security under a single delivery team. Its roots in data centers shape its approach to modernizing complex environments that span private facilities and public clouds. Engineers assess a client's existing systems, plan upgrades and build the platforms, while managed services teams monitor operations, maintain software and respond to security incidents. The company earns money through consulting and implementation projects, technology sales and ongoing services that keep those environments running. AHEAD was founded in 2007 and is headquartered in Chicago, Illinois.
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