Lambda Labs is a leading GPU cloud and ML hardware provider — sells on-prem GPU servers (Lambda Vector, DGX-class) and runs Lambda Cloud (1-Click Clusters of H100/H200/B200). Series C in 2024. The interview emphasizes data-center networking (InfiniBand, NVLink), Kubernetes-on-GPU, and the systems work that makes large GPU clusters reliable.
Process
Recruiter screen → 60-minute coding phone (Python or Go) → onsite virtual: 2 coding, 1 system design, 1 craft deep-dive, 1 behavioral. Some infra roles add a Linux/networking deep-dive. Cycle: 3–4 weeks.
What they actually ask
- Design a multi-tenant GPU cluster scheduler with InfiniBand topology awareness
- Design a high-throughput object store for ML datasets
- Design a Kubernetes-on-GPU control plane with NCCL/MIG support
- Coding: medium DSA, often with networking or scheduling framing
- Behavioral: ownership, customer empathy for ML researchers, on-call discipline
Levels and comp (2026)
- SE: $170K–$220K total (cash + late-stage equity)
- Senior SE: $230K–$310K total
- Staff: $310K–$430K total
- Principal: $440K–$610K total
Prep priorities
- Be fluent in Python (orchestration), Go (control plane), and some C/Linux internals
- Understand InfiniBand, NVLink, NCCL, and GPU topology
- Brush up on Kubernetes device plugins, MIG/MPS, and HPC scheduling (Slurm)
Frequently Asked Questions
Is Lambda remote-friendly?
Hubs in San Francisco (HQ) and Allen TX (datacenter). Many engineering roles remote within US; some require datacenter proximity.
How does Lambda compare to CoreWeave or RunPod?
CoreWeave is the largest GPU cloud (now public) and skews enterprise-Microsoft. RunPod is the developer-friendly spot/serverless option. Lambda sits in between with strong on-prem and cloud-cluster combo. Comp is mid-tier infrastructure with strong equity upside.
What is the engineering culture?
Hardware-aware, customer-driven (sells to AI labs), fast-moving. Strong on-call culture given the workloads.