[All remote jobs](https://jobicy.com/jobs.md)[![Lambda logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2025/06/7ee299dc-221-1.jpeg)](https://jobicy.com/company/lambda.md)[Lambda](https://jobicy.com/company/lambda.md) # Technical Success 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/lambda.md)ShareRemote from[USA](https://jobicy.com/job-region/usa.md)SalaryUSD 214k–285k / yrDepartment[Technical Support](https://jobicy.com/categories/technical-support.md)EmploymentFull TimeExperienceOpen levelPublished20 Oct 2026Apply before19 Nov 2026Listing views27Application 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. Lambda is seeking a Technical Success Engineer to lead strategic GPU and AI-cloud customer deployments from signed agreement through stable production operation. The role validates compute, storage, networking, connectivity, and configuration while coordinating Infrastructure, Engineering, Product, Data Center, and account teams. This person serves as the customer’s primary technical contact during deployment and early production, communicates RAG status and risks, and drives blockers to resolution. Success requires hands-on expertise in GPU/HPC infrastructure, cloud platforms, Kubernetes, or large-scale Linux systems, plus strong ownership and technical communication. The position also improves repeatable delivery through runbooks, checklists, and automation. ## 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 insightThe position combines complex infrastructure troubleshooting with end-to-end accountability for high-stakes enterprise deployments. It requires independent judgment across GPU/HPC, cloud, Linux, networking, storage, and multiple internal and customer stakeholders. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Highly competitive$249,500US market range (per year)$180k–$270k0$297k AI insightThe employer discloses an annual base salary range of $214,000–$285,000 USD, with a midpoint of $249,500. This is a credible range for a senior US-based technical customer-deployment role focused on GPU/HPC and AI cloud infrastructure. Estimated US market compensation for comparable roles is approximately $180,000–$270,000 annually. ## Core skills Skills and capabilities most closely associated with this opportunity. [GPU infrastructure](https://jobicy.com/jobs?search_keywords=GPU%20infrastructure.md)[High-performance computing](https://jobicy.com/jobs?search_keywords=High-performance%20computing.md)[Cloud platforms](https://jobicy.com/jobs?search_keywords=Cloud%20platforms.md)[Kubernetes](https://jobicy.com/jobs?search_keywords=Kubernetes.md)[Linux systems](https://jobicy.com/jobs?search_keywords=Linux%20systems.md)[Network troubleshooting](https://jobicy.com/jobs?search_keywords=Network%20troubleshooting.md)[Storage systems](https://jobicy.com/jobs?search_keywords=Storage%20systems.md)[Compute infrastructure](https://jobicy.com/jobs?search_keywords=Compute%20infrastructure.md)[Production deployments](https://jobicy.com/jobs?search_keywords=Production%20deployments.md)[Technical customer success](https://jobicy.com/jobs?search_keywords=Technical%20customer%20success.md)[Cross-functional coordination](https://jobicy.com/jobs?search_keywords=Cross-functional%20coordination.md)[Risk reporting](https://jobicy.com/jobs?search_keywords=Risk%20reporting.md)[Technical documentation](https://jobicy.com/jobs?search_keywords=Technical%20documentation.md)[Runbook development](https://jobicy.com/jobs?search_keywords=Runbook%20development.md)[Infrastructure validation](https://jobicy.com/jobs?search_keywords=Infrastructure%20validation.md) Sample interview questionsHow would you validate that a GPU cluster deployment meets the commitments made to a customer?I would begin with the signed requirements and convert them into a validation checklist covering GPU type and quantity, node configuration, network topology, storage capacity and performance, access controls, observability, and workload readiness. I would test each dependency, document exceptions, assign owners and due dates, and obtain customer confirmation after successful production validation. Describe your approach when a customer workload is failing due to an unclear networking or storage issue. I first establish impact, scope, and recent changes, then collect evidence from workload logs, cluster events, network telemetry, and storage metrics. I isolate whether the issue is configuration, connectivity, performance, permissions, or a platform defect, engage the appropriate engineering owner with a concise problem statement, and keep the customer updated on status, risk, and mitigation. How do you communicate deployment risk to both technical teams and executive stakeholders? I use a concise RAG status format that states the current condition, customer impact, blocker, accountable owner, mitigation plan, and expected decision or resolution date. Technical teams receive diagnostic detail and action items, while executive stakeholders receive the business impact, delivery risk, and any decisions needed. What would you include in a reusable deployment runbook after resolving a recurring issue? I would document the trigger conditions, prerequisites, step-by-step validation and remediation procedures, expected outputs, escalation criteria, ownership, and links to dashboards or commands. I would also automate repeatable checks where possible and review the runbook with the teams that will operate it. How do you balance customer urgency with the need to hold internal engineering teams accountable? I make the customer impact and technical evidence visible, agree on a clear owner and resolution path, and set an update cadence. I avoid assigning blame, but I am direct about dependencies, deadlines, and escalation when a blocker threatens production readiness; meanwhile, I provide the customer with transparent, actionable communication. Opportunity details ## About this role. Lambda, The Superintelligence Cloud, is a leader in AI cloud infrastructure serving tens of thousands of customers. Our customers range from AI researchers to enterprises and hyperscalers. Lambda’s mission is to make compute as ubiquitous as electricity and give everyone the power of superintelligence. One person, one GPU. If you’d like to build the world’s best AI cloud, join us. About this role The Superintelligence Technical Success Engineer is part of Lambda’s Superintelligence business unit, dedicated to our largest, most strategic customers operating in the most complex environments. This role owns taking signed deployment from contract to a live, fully operational production environment. The role requires strong technical acumen to validate the build against what was promised, spot gaps, and work effectively with engineering and infrastructure teams to get issues resolved and requirements clearly understood. You’ll work alongside the account team, to make sure the customer’s requirements and expectations are clearly understood and addressed throughout deployment. This role calls for someone who’s ready to dive in wherever the engagement needs them. What You’ll Do * Take signed deployments, from contract, to live, to working production environments, validating configuration, connectivity, storage, and compute against what was promised * Bring technical depth to validation and troubleshooting conversations — asking the right questions, spotting gaps, and working closely with engineering and infrastructure teams to drive issues to resolution * Coordinate with Infrastructure, Engineering, Product, and Data Center teams to close technical dependencies and resolve blockers * Own a current, accurate technical picture of the deployment — what’s built, what’s open, what’s at risk * Keep stakeholders informed with clear, regular status updates: RAG status, top risks, and what’s being done about them * Guide the customer through onboarding to their first successful production workload * Be the customer’s go-to technical contact through deployment and early production * Feed recurring technical patterns back into reusable runbooks, checklists, or automation * Transition out once the customer is stable and self-sufficient, keeping the broader account team informed along the way You * 4+ years of hands-on technical experience with GPU/HPC infrastructure, cloud platforms, Kubernetes, or large-scale Linux systems * Comfortable being the technical voice in the room able to validate builds, spot gaps, and hold engineering teams accountable for resolving them * Strong troubleshooting instincts and a willingness to get into the weeds of networking, storage, or compute issues * Track record of coordinating across engineering and infrastructure teams to close out technical dependencies * Clear written and verbal communication for status updates and technical documentation * A bias toward diving in and taking ownership, rather than waiting for a fully defined process Nice to Have * Exposure to large-scale GPU cluster deployments * Familiarity with project/program tracking tools and structured status reporting * Experience building runbooks or checklists that outlived the engagement they were built for Salary Range Information The annual salary range for this position has been set based on market data and other factors. However, a salary higher or lower than this range may be appropriate for a candidate whose qualifications differ meaningfully from those listed in the job description. About Lambda * Founded in 2012, with 500+ employees, and growing fast * Our investors notably include TWG Global, US Innovative Technology Fund (USIT), Andra Capital, SGW, Andrej Karpathy, ARK Invest, Fincadia Advisors, G Squared, In-Q-Tel (IQT), KHK & Partners, NVIDIA, Pegatron, Supermicro, Wistron, Wiwynn, Gradient Ventures, Mercato Partners, SVB, 1517, and Crescent Cove * We have research papers accepted at top machine learning and graphics conferences, including NeurIPS, ICCV, SIGGRAPH, and TOG * Our values are publicly available: [https://lambda.ai/careers](https://lambda.ai/careers) * We offer generous cash & equity compensation * Health, dental, and vision coverage for you and your dependents * Wellness and commuter stipends for select roles * 401k Plan with 2% company match (USA employees) * Flexible paid time off plan that we all actually use Equal Opportunity Employer Lambda is an Equal Opportunity employer. Applicants are considered without regard to race, color, religion, creed, national origin, age, sex, gender, marital status, sexual orientation and identity, genetic information, veteran status, citizenship, or any other factors prohibited by local, state, or federal law. 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