[All remote jobs](https://jobicy.com/jobs.md)[![Vultr logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2024/04/4089507a-221.jpeg)](https://jobicy.com/company/vultr.md)[Vultr](https://jobicy.com/company/vultr.md) # GPU Solutions Engineer Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/vultr.md)ShareRemote from[USA](https://jobicy.com/job-region/usa.md)SalaryUSD 180k–200k / yrDepartment[DevOps & Infrastructure](https://jobicy.com/categories/admin.md)EmploymentFull TimeExperienceSeniorPublished30 Oct 2026Apply before29 Nov 2026Listing views47Application actions1Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. Vultr is hiring a senior GPU Solutions Engineer to help customers design, deploy, and optimize AI, machine-learning, and high-performance GPU workloads on its cloud platform. The role combines deep infrastructure architecture with customer-facing solution engineering, partnering with sales, customer success, product, engineering, support, and marketing. Key technical areas include Kubernetes, Slurm, GPU benchmarking, AI/ML frameworks, Linux clusters, high-performance storage, IaC, and high-speed networking. The engineer will lead architectural discussions, resolve complex production challenges, create technical enablement content, and travel up to 25%. This is a highly visible, fast-paced role requiring independent ownership and strong stakeholder communication. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightThis is a senior technical consulting role requiring expertise across GPU hardware, distributed AI workloads, cluster networking, orchestration, and customer solution design. The candidate must also translate complex architecture into business value while independently leading high-impact customer engagements. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Market rate$190,000US market range (per year)$170k–$220k0$242.00k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. The employer discloses a compensation range of $180,000–$200,000 USD. The yearly cadence is inferred from the compensation magnitude, US-based remote hiring location, and senior full-time solutions engineering context. A reasonable estimated US market range for comparable GPU and cloud solutions engineering roles is $170,000–$220,000 per year. ## Core skills Skills and capabilities most closely associated with this opportunity. [GPU computing](https://jobicy.com/jobs?search_keywords=GPU%20computing.md)[Cloud infrastructure](https://jobicy.com/jobs?search_keywords=Cloud%20infrastructure.md)[Kubernetes](https://jobicy.com/jobs?search_keywords=Kubernetes.md)[Slurm](https://jobicy.com/jobs?search_keywords=Slurm.md)[AI/ML workloads](https://jobicy.com/jobs?search_keywords=AIML%20workloads.md)[GPU benchmarking](https://jobicy.com/jobs?search_keywords=GPU%20benchmarking.md)[Terraform](https://jobicy.com/jobs?search_keywords=Terraform.md)[Ansible](https://jobicy.com/jobs?search_keywords=Ansible.md)[Linux](https://jobicy.com/jobs?search_keywords=Linux.md)[x86 server architecture](https://jobicy.com/jobs?search_keywords=x86%20server%20architecture.md)[InfiniBand](https://jobicy.com/jobs?search_keywords=InfiniBand.md)[RoCE networking](https://jobicy.com/jobs?search_keywords=RoCE%20networking.md)[NCCL optimization](https://jobicy.com/jobs?search_keywords=NCCL%20optimization.md)[High-performance storage](https://jobicy.com/jobs?search_keywords=High-performance%20storage.md)[Solutions engineering](https://jobicy.com/jobs?search_keywords=Solutions%20engineering.md) Sample interview questionsHow would you design a GPU cluster for distributed large-language-model training?I would begin by defining model size, training data volume, target throughput, resiliency needs, and budget. I would then select GPU topology and node configuration, design high-bandwidth east-west networking such as InfiniBand or RoCE, configure shared high-performance storage, and use Kubernetes or Slurm for scheduling. Finally, I would benchmark NCCL communication, GPU utilization, storage throughput, and end-to-end training performance before production rollout. Describe how you would troubleshoot poor multi-node GPU training performance. I would establish a baseline with single-node performance and then isolate compute, network, storage, and software factors. I would inspect GPU utilization, NCCL logs, topology awareness, network counters, RDMA configuration, CPU affinity, data-loader behavior, and storage latency. Based on the evidence, I would tune communication settings, placement policies, networking configuration, or the workload pipeline and validate improvements through repeatable benchmarks. How do you explain a complex GPU architecture to an executive stakeholder? I frame the design around the business outcome, such as reducing model-training time, increasing inference capacity, or improving cost predictability. I avoid unnecessary implementation detail and use a concise architecture diagram, clear assumptions, expected performance metrics, risks, and phased recommendations. Technical depth is available for engineering stakeholders, but the executive discussion remains focused on value, trade-offs, and decisions needed. What is your approach to moving a customer's AI workload from proof of concept into production? I first define success criteria and assess the proof of concept for reliability, security, data movement, observability, cost controls, and operational ownership. I then create a production architecture with automated infrastructure, workload orchestration, monitoring, access controls, capacity planning, and documented runbooks. A staged rollout with load testing and stakeholder checkpoints reduces risk while ensuring the customer can operate the platform confidently. How have you used infrastructure as code in a customer solution? I use tools such as Terraform to make infrastructure provisioning consistent, reviewable, and repeatable, and Ansible or similar tooling to configure hosts and software layers. For a GPU platform, I would codify networking, compute instances, storage, Kubernetes or Slurm configuration, monitoring, and access policies. This shortens delivery time, limits configuration drift, and gives the customer a maintainable reference implementation. Opportunity details ## About this role. ### Who We Are Vultr is on a mission to make high-performance cloud infrastructure easy to use, affordable, and locally accessible for enterprises and AI innovators around the world. With 33 global cloud data center locations, Vultr is trusted by hundreds of thousands of active customers across 185 countries for its flexible, scalable, global Cloud Compute, Cloud GPU, Bare Metal, and Cloud Storage solutions. In December 2024 Vultr announced an equity financing at a $3.5 billion valuation. Founded by David Aninowsky and self-funded for over a decade, Vultr has grown to become the world’s largest privately-held cloud infrastructure company. Vultr Cares * 100% company-paid insurance premiums for employee medical, dental and vision plans. * 401(k) plan that matches 100% up to 4%, with immediate vesting * Professional Development Reimbursement of $2,500 each year * 11 Holidays + Paid Time Off Accrual + Rollover Plan * Commitment matters to Vultr! Increased PTO at 3 year and 10 year anniversary + 1 month paid sabbatical every 5 years + Anniversary Bonus each year * $500 stipend for remote office setup in first year + $400 each following year * Internet reimbursement up to $75 per month * Gym membership reimbursement up to $50 per month * Company paid Wellable subscription ### Join Vultr Vultr is seeking a highly skilled and experienced GPU Solutions Engineer to lead customers designing, implementing, and optimizing solutions on Vultr’s GPU platform. This role partners closely with Direct Sales, Customer Success, Product Management, Engineering and Support Departments to bring AI, ML, GPU workloads to life. The ideal candidate is technically strong and highly collaborative. You have significant hands on experience in cloud infrastructure, networking, Kubernetes and modern GPU computing architectures. You are comfortable translating complex technical concepts into clear solutions, solving real-world challenges, guiding customers from initial design into production and collaborating internally and externally. This is a highly visible role in a high-growth technology company, which will require strong problem-solving skills, customer engagement, and the ability to operate independently in a fast-paced environment. You will directly influence customer adoption and the overall experience on Vultr’s platform. This is your opportunity to join our fast growing team and leave your mark on Vultr and the future of Cloud Infrastructure. Key Responsibilities * Engage collaboratively with customers to understand their objectives. * Create innovative Solutions and Architectures to solve complex Customer use cases. * Establish relationships within Customers independent of Account Executives. * Educate customers on the value that Vultr provides and expand their horizons about the art of the possible. * Participate in deep architectural discussions and design exercises to create world-class solutions at Vultr. * Collaborate with Engineering and Product Management to deliver Voice of the Customer to guide Product and Feature development. * Lead resolution of complex customer challenges. * Partner with Marketing to create reference architectures, white papers, workshops, and demonstrations for internal and external uses. * Travel 25% Qualifications * Highly motivated / self-starter with a sense of ownership, willingness to learn, and desire to succeed. * A collegial and collaborative approach working across Departments and Seniority levels within Vultr’s Customers and Vultr. * Skilled at influencing, guiding, and facilitating stakeholders and peers with decision making. * Ability to articulate technical and business concepts to diverse stakeholders. * Demonstrated willingness and ability to dig into unfamiliar territories to solve complex challenges. * Experience with structured sales engagement models MEDDPIC, Sandler, BANT, etc. * Expertise in the GPU ecosystem from GPU Hardware, Bare Metal and Virtualized Cloud Delivery, AL & ML frameworks and AI & ML Ops orchestration. * Extensive knowledge of AI Models / Algorithms, Libraries, Compilers and Runtimes for diverse silicon ecosystems. * GPU benchmarking and workload testing experience. * Demonstrated experience with workload orchestration, Kubernetes and Slurm. * System and Cluster level understanding of x86 server hardware architecture and Linux OS. * Hands on with Infrastructure as Code methodologies including, Terraform, Ansible, etc. * Networking experience, including knowledge of Infiniband, RoCE / UEC Ethernet, or other networking protocols. * NCCL / RCCL performance optimization. * High Performance Storage experience, knowledge of performant multi-user offerings including Open Source and COTS options. * 5+ years of design, implementation, or consulting in applications and infrastructure experience * 4+ years of Solution Engineering, Sales Engineering, Professional Services or Consulting experience * 3+ year of experience deploying GPU-based AI, ML & Analytics solutions (e.g., for Training, Inference, Fine Tuning, Reinforcement Learning, Agentic Harnesses) Compensation $180,000 – $200,000 Final compensation will vary depending on years of experience, background/skill set, location, and applicable laws. ### Inclusion & Privacy We are an equal opportunity employer and are committed to creating an inclusive environment for all employees. We welcome applications from individuals of all backgrounds and experiences, and we prohibit discrimination based on race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected status under applicable laws. Vultr will consider qualified applicants with arrest or conviction records in accordance with applicable laws and will not conduct a background check until after an offer of employment has been extended and accepted. We also take your privacy seriously. We handle personal information responsibly and follow applicable laws, including U.S. privacy rules and India’s Digital Personal Data Protection Act, 2023. Your data is used only for legitimate business purposes and is protected with proper security measures. Where allowed by law, applicants may request details about the data we collect, access or delete their information, withdraw consent for its use, and opt out of nonessential communications. For more details, please see our [Privacy Policy](https://www.vultr.com/legal/privacy/). 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