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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.
Key Responsibilities
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
Required Qualifications
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
Preferred Qualifications
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
Skills Required
- Bachelor's degree in computer science, computer engineering, electrical engineering, or a related technical field, or equivalent practical experience
- 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 infrastructure layers in a physical datacenter environment
- Ability to document systems clearly and communicate effectively with technical and nontechnical stakeholders
- 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 Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about AHEAD and has not been reviewed or approved by AHEAD.
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Retirement Support — 401(k) contributions are matched dollar-for-dollar on the first $5,000 each year, with matching made each pay period and immediate 100% vesting. This structure signals above-standard employer support for retirement savings.
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Affordable Benefits — Medical options include low employee premiums for PPO and HDHP plans, and the HDHP adds employer HSA funding plus a dollar-for-dollar HSA match up to stated amounts. Dental and vision plans list very low per-paycheck costs, helping keep overall healthcare spend manageable.
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Wellbeing & Lifestyle Benefits — No-cost telemedicine (including virtual mental health when enrolled), free Calm access for the employee and dependents, and an EAP with counseling are included. Company-paid life and disability plus voluntary protections (legal/ID, pet insurance) and other extras round out a comprehensive set of supports.
AHEAD Insights
What We Do
AHEAD builds platforms for digital business. By weaving together cloud infrastructure, intelligent operations, and modern applications, we help enterprises deliver on the promise of digital transformation.
