What You'll Do
We are seeking an engineer with extensive experience in cloud infrastructure to build and optimize GPU-first compute systems. In this role, you will be responsible for:
Designing and implementing a highly available and reliable GPU and CPU “host and instance lifecycle” control plane.
Guide technical decisions involving semiconductor architecture, BIOS/Firmware settings, system boot methodologies, and DPU utilization to optimize host capabilities, performance and reliability.
Guide design of compute platform multi-tenant security model
Provide technical leadership and mentorship for senior engineers across several teams to execute on complex infrastructure roadmaps and technical strategy.
Collaborate with product and data center organizations to translate customer requirements into scalable infrastructure capabilities.
Work with customers on translating vague customer technical requirements into concrete engineering deliverables.
Set engineering standards and lead design reviews for mission-critical cloud software at scale.
Who You are
10+ years of experience working on compute control plane distributed systems used for deploying and lifecycle managing heterogeneous compute platforms into data-centers, built for resilience at scale.
Deep expertise in durable execution models and distributed systems used in cloud-service provisioning.
Basic knowledge of software defined networking fundamentals that informs secure, multi-tenant distributed systems.
Proven track record of leading large-scale semi-conductor hardware enablement and deployment initiatives.
Proven experience in deploying net-new data-centers into a global compute platform (not just working in existing data-centers).
Proficiency in one of more of the following programming languages: C/C++, Rust, Python, Go.
Nice to Have
Knowledge of Nvidia’s AI Factory architectural components (including GPU hosts, CPU hosts, SuperNICs (ConnectX and Bluefield DPUs , and switches).
Knowledge of Nvidia’s AI Factory software offerings (like DOCA, DOCA SNAP, CUDA, et al.)
Knowledge of Linux kernel internals, device drivers, and virtualization technologies (KVM, QEMU), kernel bypass technologies (like SR-IOV, DPDK, SPDK).
Experience with Cloud Service Provider Kubernetes offerings.
Knowledge of high-performance networking (InfiniBand, RoCE) and storage protocols (NVMe-oF).
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.
