[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) # Strategic Technical Account Manager GPU 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 115k–140k / yrDepartment[Technical Support](https://jobicy.com/categories/technical-support.md)EmploymentFull TimeExperienceOpen levelPublished22 Oct 2026Apply before21 Nov 2026Listing views44Application actions2Application 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. This is a senior customer-facing Technical Account Manager role focused on the post-sales success of customers running large-scale AI and GPU workloads. The role combines cloud infrastructure architecture, GPU cluster performance optimization, distributed ML expertise, and long-term technical account ownership. The successful candidate will advise on NVIDIA and AMD GPU environments, Kubernetes, SLURM, high-speed networking, storage, and distributed-training frameworks. They will also coordinate urgent incident escalations, communicate with executive and engineering stakeholders, and support account expansion through technical validation. The position is remote within the United States and requires strong independent judgment in high-impact customer environments. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 4/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightThe role demands deep practical expertise across GPU hardware, distributed AI training, high-performance networking, storage, and cloud operations. It also requires managing high-urgency incidents and translating complex technical tradeoffs for both customers and internal business teams. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Market rate$127,500US market range (per year)$115k–$155k0$170.50k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. The employer discloses a salary range of $115,000–$140,000 USD. Although no cadence is explicitly stated, yearly pay is inferred from the amount, US location, full-time classification, and professional seniority of the role. The estimated US market range for a GPU-focused technical account manager is $115,000–$155,000 per year, depending on depth of AI infrastructure expertise and customer portfolio scope. ## Core skills Skills and capabilities most closely associated with this opportunity. [Technical account management](https://jobicy.com/jobs?search_keywords=Technical%20account%20management.md)[GPU infrastructure](https://jobicy.com/jobs?search_keywords=GPU%20infrastructure.md)[NVIDIA GPUs](https://jobicy.com/jobs?search_keywords=NVIDIA%20GPUs.md)[AMD GPUs](https://jobicy.com/jobs?search_keywords=AMD%20GPUs.md)[CUDA](https://jobicy.com/jobs?search_keywords=CUDA.md)[ROCm](https://jobicy.com/jobs?search_keywords=ROCm.md)[Distributed training](https://jobicy.com/jobs?search_keywords=Distributed%20training.md)[Kubernetes](https://jobicy.com/jobs?search_keywords=Kubernetes.md)[SLURM](https://jobicy.com/jobs?search_keywords=SLURM.md)[PyTorch](https://jobicy.com/jobs?search_keywords=PyTorch.md)[High-performance networking](https://jobicy.com/jobs?search_keywords=High-performance%20networking.md)[InfiniBand](https://jobicy.com/jobs?search_keywords=InfiniBand.md)[RoCE](https://jobicy.com/jobs?search_keywords=RoCE.md)[High-performance storage](https://jobicy.com/jobs?search_keywords=High-performance%20storage.md)[Incident management](https://jobicy.com/jobs?search_keywords=Incident%20management.md) Sample interview questionsHow would you diagnose poor scaling efficiency in a multi-node GPU training cluster?I would first establish a baseline for GPU utilization, step time, data-loading latency, network throughput, and storage IOPS. I would then isolate whether the bottleneck is caused by communication collectives, topology, CPU or memory contention, data pipeline throughput, or inefficient parallelization, and validate improvements through controlled benchmark runs. How do you advise a customer choosing between GPU types and cluster configurations for training versus inference? I begin with model size, precision requirements, latency and throughput targets, dataset characteristics, framework support, and budget constraints. For training, I prioritize memory capacity, interconnect bandwidth, and scalable distributed communication; for inference, I focus more on latency, batch behavior, model optimization, and cost per request. Describe how you would manage a high-severity customer incident involving a GPU cluster outage. I would establish ownership, assess customer impact, engage the appropriate SRE, networking, support, and engineering teams, and provide a clear communication cadence. After service restoration, I would lead a customer-facing review covering root cause, corrective actions, prevention measures, and any capacity or architecture changes needed to reduce recurrence. What factors would you review when optimizing a distributed PyTorch workload? I would review data-loader performance, batch size, mixed precision, gradient accumulation, checkpointing, GPU memory use, communication backend settings, node and GPU topology, and network fabric health. I would use profiling data to prioritize the largest bottleneck and measure the effect of each change on throughput, convergence, and cost. How do you balance technical advocacy for a customer with account-growth objectives? I build trust by recommending the architecture that best meets the customer's technical and economic goals, even when that means starting small. Once measurable value is established, I identify expansion opportunities such as additional capacity, storage, networking, or reservations that directly support the customer's roadmap. 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 The GPU-focused Technical Account Manager (TAM) leads the post-sales technical success of customers deploying large-scale AI, training, inference, and high-performance GPU workloads on the company’s platform. This includes customers using NVIDIA GPU clusters, AMD GPU clusters, GPU VMs, and rack-scale bare-metal environments. You will act as a trusted advisor across LLM training, fine-tuning, RAG workloads, distributed training frameworks, storage throughput requirements, multi-GPU scaling, and performance tuning. This role requires deep technical fluency and exceptional customer management skills to help AI/ML teams achieve predictable, cost-efficient, high-performance outcomes. Key Responsibilities AI/GPU Onboarding & Workload Architecture * Lead onboarding for customers deploying GPU clusters (bare metal, VMs, or hybrid). * Advise on cluster design: multi-GPU topology, NVLink/NVSwitch considerations, RDMA, Infiniband and RoCE Ethernet, networking throughput, and storage IOPS requirements. * Guide customers in selecting GPU types and configurations based on workload (training, fine-tuning, inference, embeddings, RAG pipelines). * Support distributed frameworks: PyTorch, TensorFlow, DeepSpeed, Megatron, JAX, Ray, Mosaic, HuggingFace, etc. * Advanced hands on Kubernetes skills * Advanced hands on SLURM skills Performance Optimization & Scaling * Identify bottlenecks (network, storage, memory bandwidth). * Provide tuning recommendations for batch size, mixed precision, parallelization strategies, and checkpointing. * Help customers evaluate cost vs. performance tradeoffs (GPU mix, CPU pairing, instance types, cluster sizing). Technical Relationship Ownership * Own the long-term technical strategy across assigned GPU/AI accounts, including hyperscalers, labs, and high-growth AI startups. * Host recurring technical review meetings, roadmap reviews, and optimization sessions. * Define scaling plans, future GPU reservation needs, and capacity forecasting. * Incident & Escalation Management * Partner with Support, SRE, Networking, NOC, and Product Management & Engineering to resolve high-urgency incidents. * Manage outage communications, corrective action plans, and postmortem reviews with customers. * Advocate for GPU reliability improvements and influence roadmap priorities. Account Growth & Expansion * Identify opportunities for expanded clusters, high speed storage, or networking upgrades. * Support Sales with technical validation and architecture diagrams needed for expansion. Customer Advocacy & Product Feedback * Provide structured feedback on existing and future GPU offerings, networking fabrics, storage platforms, and upcoming AI/ML platform features. * Partner with Product on early access programs (new GPUs, pipelines, orchestration, etc.). Qualifications * 2–5+ years as an AI/ML Engineer, AI/ML Ops, Technical Account Manager, HPC Engineer, Sales/Solutions Engineer or relevant technical role. * Strong knowledge of GPU hardware architectures (NVIDIA/AMD), CUDA/ROCm, distributed training, and ML frameworks. * Experience with Linux tuning, networking (Infiniband, RoCE fabrics). * Experience with high-performance storage systems (DDN, NetApp, Vast, Weka, etc.). * Ability to communicate complex concepts clearly to both executives and engineering teams. * Prior experience supporting hyperscale, AI labs, or large cluster deployments is a plus. * Cloud Native Computing Foundation Certified Kubernetes Administrator (CKA) certification is a plus. Compensation $115,000 – $140,000 This salary can vary based on location, years of experience, background and skill set. ### 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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