About the job
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, San Francisco, or Bellevue office location 4 days per week; Lambda’s designated work from home day is currently Tuesday.
Responsibilities
- Architect and define scalable compute platforms optimized for AI/ML, simulation, and high-throughput workloads.
- Develop compute system standards and design patterns to ensure consistency, performance, and maintainability across infrastructure.
- Evaluate emerging CPU, GPU, and accelerator technologies, owning architectural tradeoff decisions that impact compute density, power, cooling, and total cost.
- Collaborate with product and engineering teams to map workload requirements to compute platform capabilities across bare metal and cloud deployments.
- Experience converting ambiguous business or customer needs into measurable platform requirements, technical specifications, acceptance criteria, and architecture decisions.
- Define compute platform roadmaps and architectural reference designs that guide hardware selection, firmware baselines, rack-level, and cluster design.
- Act as a technical lead during new platform introductions, guiding validation and performance characterization efforts.
- Mentor systems engineers and cross-functional stakeholders on compute performance tuning, sizing, and architectural decisions.
Qualifications
Minimum
- Proven experience (7+ years) architecting large-scale 10k-100k+ GPU HPC or cloud compute platforms.
- Deep knowledge of CPU/GPU architectures, memory hierarchies, and accelerator topologies.
- Experience designing systems around high-bandwidth, low-latency fabrics (NVLink, InfiniBand, and RoCE).
- Strong understanding of system performance tuning, resource scheduling, thermal and power optimization, and compute lifecycle management.
- Comfortable working across hardware and software boundaries, especially at the intersection of compute architecture, OS behavior, and orchestration layers.
- Skilled at balancing architectural tradeoffs for density, power efficiency, cooling, and performance.
- Strong analytical and communication skills, with a track record of influencing technical strategy across teams.
- Strong ownership and can do attitude, self-starter who feels comfortable working in ambiguity.
Preferred
- Hands-on experience with AI/ML workloads and their compute performance characteristics.
- Familiarity with orchestration tools used in HPC. (Slurm, Kubernetes, etc)
- Experience with virtualization technologies, specifically GPU virtualization.
- Exposure to hardware validation, vendor collaboration, and long-term OEM roadmap alignment.
- Background in compute telemetry, real-time performance profiling, or large-scale A/B infrastructure testing.