HPC Solutions Engineer

HPC Solutions Engineer

Location: Dallas, TX - relocation package offered for non-local candidates

Work Arrangement: Hybrid

Employment Type: Direct Hire

Compensation: Competitive Base Salary + Performance Bonus

Benefits: 100% Company-Paid Benefits

Overview

Our client is seeking an HPC Customer Solutions Engineer to lead the technical design, integration, and delivery of high-performance computing and AI infrastructure solutions.

This is a highly technical, customer-facing role focused on designing scalable architectures across GPU/CPU compute, storage, networking, Kubernetes, orchestration, and security. The position spans the full solution lifecycle from technical discovery and workload analysis through proof-of-concept, deployment, and ongoing optimization.

The ideal candidate brings deep HPC and AI infrastructure expertise, strong hands-on system design and performance tuning experience, and the ability to translate complex customer requirements into scalable, production-ready solutions.

Key Responsibilities

Customer Engagement & Technical Discovery

• Work directly with customers to understand workload requirements, performance targets, and technical objectives.
• Lead technical discovery sessions focused on application behavior, bottlenecks, scalability, and infrastructure requirements.
• Serve as a trusted technical advisor throughout the solution lifecycle.

Solution Architecture

• Design end-to-end HPC and AI architectures across compute, storage, networking, orchestration, and security.
• Recommend hardware and software solutions aligned with performance, scalability, reliability, and efficiency goals.
• Develop architecture blueprints, integration plans, and technical documentation.
• Design solutions supporting GPU-intensive AI/ML, LLM, and advanced compute workloads.

Performance & Workload Optimization

• Support proof-of-concept, benchmarking, and performance-validation initiatives.
• Perform workload profiling, system tuning, and infrastructure optimization.
• Identify bottlenecks across compute, storage, networking, and orchestration layers.
• Recommend improvements that increase workload performance, scalability, and resilience.

Implementation & Delivery

• Provide technical leadership during deployment and integration.
• Partner with customers and internal Engineering, Product, and Operations teams throughout implementation.
• Support solutions from architecture through production deployment and optimization.
• Troubleshoot complex infrastructure and workload issues during delivery.

Technical Leadership

• Maintain expertise across emerging HPC, AI, GPU, storage, networking, and orchestration technologies.
• Build relationships with technology partners across GPU, networking, and storage ecosystems.
• Contribute to reference architectures, reusable design patterns, and technical best practices.
• Lead customer workshops, architecture reviews, and technical presentations.

Required Qualifications

• Experience designing HPC, AI infrastructure, large-scale compute, or distributed systems solutions.
• Strong technical expertise across:
• GPU and CPU architectures
• NVIDIA / CUDA ecosystem
• Slurm and Kubernetes
• InfiniBand, RDMA, and RoCE
• Lustre, GPFS / Spectrum Scale, Ceph, VAST, or similar storage platforms
• Kubernetes and container orchestration
• Identity, encryption, and infrastructure security
• Strong Linux systems knowledge, including tuning and performance analysis.
• Experience translating workload requirements into detailed technical architectures.
• Experience with proof-of-concept, benchmarking, or workload optimization.
• Strong customer-facing communication and presentation skills.
• Ability to work effectively with engineering, product, operations, and executive stakeholders.

Preferred Experience

• AI/ML, LLM, GPU, or HPC workloads.
• NVIDIA GPU infrastructure.
• Containerized HPC environments.
• Automation and Infrastructure-as-Code.
• Workload migration and performance engineering.
• Next-generation GPU and high-speed interconnect technologies.
• Bachelor's or Master's degree in Computer Science, Engineering, Physics, or related field.
• Relevant cloud, Linux, networking, Kubernetes, or security certifications.