Customer Solutions Engineer HPC & Cloud

This role serves as a trusted technical advisor to customers, helping them design, deploy, optimize, and scale compute-intensive workloads ranging from large-scale AI/ML training and inference to advanced scientific simulations and data-intensive research platforms. This person should be equally comfortable discussing architecture with infrastructure engineers, troubleshooting workload performance with technical teams, presenting solutions to customer leadership, and collaborating internally with product and engineering.

Customer Solutions Engineer
Location: Dallas, TX
Work Arrangement: Hybrid
Employment Type: Direct Hire
Relocation: Available for qualified non-local candidates
Overview
Our client is seeking a Customer Solutions Engineer to support strategic customers across high-performance computing, cloud, GPU, and AI infrastructure environments.

This role serves as a trusted technical advisor to customers, helping them design, deploy, optimize, and scale compute-intensive workloads ranging from large-scale AI/ML training and inference to advanced scientific simulations and data-intensive research platforms.

Sitting at the intersection of engineering, sales, product, and customer success, the Customer Solutions Engineer will combine strong technical depth with a customer-first mindset. This person will work directly with technical teams and business stakeholders to solve complex infrastructure challenges, optimize workload performance, and ensure customers are getting maximum value from the platform.

This is an ideal opportunity for a highly technical engineer who enjoys solving difficult problems, working directly with customers, and building long-term strategic relationships.
Key ResponsibilitiesCustomer Solutions & Technical Advisory

• Serve as a primary technical advisor for strategic and enterprise customers.
• Partner with customers to design, deploy, optimize, and scale HPC, cloud, GPU, and AI/ML workloads.
• Troubleshoot complex issues across compute, networking, storage, Kubernetes, cloud, and application layers.
• Help customers optimize performance for AI/ML training, inference, simulation, and other GPU-intensive workloads.
• Guide customers through architecture decisions, platform integrations, scalability challenges, and infrastructure best practices.
• Translate customer business and technical requirements into practical, scalable solutions.
• Lead technical discussions around performance, reliability, availability, and platform architecture.

HPC, GPU & Cloud Engineering

• Support large-scale HPC and accelerated computing environments.
• Work with GPU-based infrastructure supporting AI, machine learning, and high-performance workloads.
• Advise customers on workload placement, resource utilization, orchestration, and performance optimization.
• Support environments leveraging Kubernetes and HPC schedulers or orchestration platforms.
• Work with public cloud technologies across AWS, Azure, and GCP where applicable.
• Use Python and automation tooling to assist with troubleshooting, validation, integration, and solution development.
• Support Infrastructure as Code and configuration automation using technologies such as Terraform and Ansible.

Sales & Solution Engineering

• Partner with sales teams during both pre-sales and post-sales customer engagements.
• Participate in technical discovery sessions to understand customer requirements, workloads, and infrastructure challenges.
• Develop solution architectures and technical recommendations aligned with customer needs.
• Deliver technical presentations, architecture reviews, demonstrations, and solution walkthroughs.
• Help customers evaluate new platform capabilities and determine how they can support current and future workloads.
• Identify opportunities to expand adoption within strategic customer environments.

Product & Engineering Collaboration

• Serve as a bridge between customers and internal product and engineering teams.
• Capture customer feedback, technical requirements, and feature requests.
• Surface recurring technical challenges and customer pain points.
• Help internal teams understand how customers are using the platform in real-world environments.
• Partner with engineering and product teams to improve platform functionality, performance, reliability, and usability.
• Provide feedback that helps influence future product capabilities and technical roadmaps.

Strategic Customer Engagement

• Build trusted relationships with engineers, architects, technical leaders, and executive stakeholders.
• Coordinate across customer teams, engineering, product, sales, and support to resolve complex issues.
• Manage technical escalations and help remove blockers affecting strategic accounts.
• Navigate competing technical requirements and guide stakeholders toward scalable, practical solutions.
• Maintain strong communication throughout customer implementations, deployments, and ongoing platform usage.

Documentation & Technical Enablement

• Develop and maintain technical documentation, reference architectures, implementation guides, and best-practice materials.
• Create reusable technical assets that support customer onboarding, deployment, and adoption.
• Document architectural decisions, troubleshooting procedures, and platform recommendations.
• Share customer lessons learned and technical best practices across internal teams.

Required Qualifications

• Strong experience in a customer-facing technical role such as Customer Solutions Engineer, Solutions Architect, Cloud Solutions Architect, Customer Engineer, Technical Account Manager, Solutions Engineer, Sales Engineer, or similar.
• Hands-on experience working with HPC, cloud infrastructure, GPU computing, or large-scale technical environments.
• Strong knowledge of Kubernetes and containerized infrastructure.
• Experience programming or scripting with Python.
• Experience with Infrastructure as Code and automation tools such as Terraform and Ansible.
• Strong understanding of GPU and HPC computing concepts.
• Familiarity with AI/ML training, inference, and compute-intensive workloads.
• Knowledge of technologies such as CUDA, MPI, or similar accelerated and distributed computing technologies.
• Ability to troubleshoot complex issues across infrastructure, platform, and workload layers.
• Strong understanding of cloud, networking, compute, storage, and distributed systems concepts.
• Excellent customer-facing communication and presentation skills.
• Ability to explain complex technical concepts clearly to both highly technical and non-technical audiences.
• Proven ability to build trusted relationships with customers and internal stakeholders.
• Strong problem-solving skills with the ability to operate effectively in complex, fast-moving environments.
• Ability to collaborate across engineering, product, sales, support, and customer teams.

Preferred Qualifications

• Experience with HPC scheduling and orchestration technologies such as Slurm.
• Experience with ML orchestration platforms such as Kubeflow.
• Experience working with MPI-based applications or distributed computing environments.
• Hands-on experience with NVIDIA GPUs and accelerated computing platforms.
• Experience supporting large GPU clusters or AI infrastructure.
• Experience with PyTorch, TensorFlow, or similar machine learning frameworks.
• Experience with AWS, Microsoft Azure, or Google Cloud Platform.
• Understanding of networking and storage architectures supporting HPC and AI workloads.
• Experience with hybrid cloud or multi-cloud environments.
• Experience supporting AI/ML platforms, research computing environments, or scientific computing workloads.
• Strong project management and organizational skills with the ability to support multiple strategic customers.
• Experience managing technical escalations and complex customer implementations.
• Experience mentoring engineers or contributing to technical team development.

Ideal Candidate Profile
The ideal candidate is a highly technical, customer-facing engineer who can operate comfortably across HPC, cloud, GPU infrastructure, Kubernetes, and AI/ML workloads.

This person should be equally comfortable discussing architecture with infrastructure engineers, troubleshooting workload performance with technical teams, presenting solutions to customer leadership, and collaborating internally with product and engineering.

The strongest candidates will combine:

• Deep technical curiosity
• Strong HPC, GPU, cloud, or infrastructure knowledge
• Hands-on troubleshooting ability
• Excellent customer communication skills
• A consultative and solutions-oriented mindset
• The ability to translate complex technical capabilities into practical business outcomes

Success in this role requires someone who can earn technical credibility quickly, solve difficult infrastructure and workload challenges, and become a trusted long-term advisor to strategic customers.