Lambda
Senior HPC Platform Hardware Engineer
San Jose Office (Zanker) · Senior
Sponsorship not specifiedDetected 1 day ago
Cloud PlatformsMachine LearningSupply ChainHardware DesignResearch
About the role
- 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.
Responsibilities
- 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.
- 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.
- Serve as the technical lead to evaluate, enable, and prototype new hardware in labs.
- 5 years of technical lead experience on hardware NPI and deployment for HPC, data center, or cloud infrastructure products, familiar with hardware NPI processes.
- 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.
Nice to have
- Experience supporting AI/ML infrastructure and accelerated compute hardware (e.g., NVIDIA, AMD, Intel).
- 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.
Skills
- Lambda's mission is to make compute as ubiquitous as electricity and give everyone the power of superintelligence.
- One person, one GPU.
Compensation
- 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.
- We offer generous cash & equity compensation
Benefits
- We have research papers accepted at top machine learning and graphics conferences, including NeurIPS, ICCV, SIGGRAPH, and TOG
- Health, dental, and vision coverage for you and your dependents
- Wellness and commuter stipends for select roles
- Flexible paid time off plan that we all actually use
Company info
- We offer generous cash & equity compensation
- 401k Plan with 2% company match (USA employees)
Equal opportunity
- 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.
- Equal Opportunity Employer
Visa & Work Authorization
- ational 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
This listing is sourced directly from Lambda's careers page and normalized into a canonical job model.