Senior HPC Platform Hardware Engineer

Job description

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. *Note: This position requires presence in our San Jose office location 4 days per week; Lambda’s designated work from home day is currently Tuesday. Hardware Engineering at Lambda is responsible for building and scaling the physical infrastructure behind the Superintelligence Cloud. Our scope spans the full hardware lifecycle: roadmap and architecture, proof-of-concept for state-of-the-art platforms, new product introduction (NPI), and fleet-scale maintenance — all engineered for gigawatt-scale AI factories with rack-first design, advanced liquid cooling, and next-generation interconnects at the cutting edge of the industry. If you want your hardware work running at the frontier of AI compute, at a scale few teams in the industry operate at, this is that team. What You’ll Do - Serve as the hands-on technical lead for integrating OEM and white-label HPC AI/ML, general purpose compute, storage, and network hardware into Lambda’s HPC platform reference architectures. - Drive the end-to-end process of new product introduction (NPI) for hardware systems, including system bring-up, documentation, vendor technical engagement, production readiness, and closure of hardware risks. - Identify, debug, and resolve hardware issues across different hardware engineering domains during hardware NPI; support closure of critical fleet issues that require hardware design, vendor corrective action, or platform configuration changes. - Partner with HPC architects to translate platform blueprints into concrete hardware selections and system configurations. - Partner with the supply chain team on new vendor evaluation and QBR/HBR feedback on established vendors. - Own the hardware platform through NPI, working with PMO to de-risk execution, drive cross-functional closure of hardware readiness issues, and ensure platforms reach production on schedule. - Collaborate with the quality team and fleet reliability team during hardware NPI and after production to continuously improve product quality and reliability at scale. - Work cross-functionally with fleet engineering, deployment, operation and datacenter engineering teams to ensure on-time delivery and deployment, quality, compatibility, performance, and scalability of new systems. - Serve as the technical lead to evaluate, enable, and prototype new hardware in labs. - Review BOMs to ensure configuration accuracy, component compatibility, and alignment of key commodities and components to Lambda platform requirements. You - 5 years of technical lead experience on hardware NPI and deployment for HPC, data center, or cloud infrastructure products, familiar with hardware NPI processes. - Possess deep knowledge and hands-on experiences in one or many of the following hardware platforms: AI/ML, general compute (x86 and ARM), storage systems, or network switches. - Broad hardware engineering domain knowledge in one or many of the below areas: electrical, thermal, mechanical, power, signal integrity, safety, compliance, reliability and manufacturing. - Are comfortable working hands-on in labs to enable and bring up new hardware systems. - Experiences in identifying, triaging and root causing hardware issues during NPI and at scale in the fleet. - Experience in PLM systems and BOM structure. - Collaborate well cross functionally to deliver production-ready hardware solutions. - Strong ownership and can do attitude, self-starter who feels comfortable working in ambiguity. Nice to Have - 10+ years of technical lead experience on hardware NPI and deployment for HPC, data center, or cloud infrastructure products, familiar with hardware NPI processes. - Experience supporting AI/ML infrastructure and accelerated compute hardware (e.g., NVIDIA, AMD, Intel). - Experience in rack scale server development and liquid cooling designs. - Exposure to fleet observability, BMC/BIOS/Network configuration and automation. - Background in performance tuning, benchmarking, and systems validation workflows. - Can interpret platform-level architecture requirements and select or adapt OEM and white-label solutions to fit. - Prior experience contributing to reference designs or large-scale infrastructure blueprints. - Are experienced with vendor-led product development cycles and can drive hardware evaluation, risk mitigation, and feedback into roadmap decisions. 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 - 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.