Impress employers and recruiters.
Choose from hundreds of resume examples.

Target Resume Now

Tailor your resume to this Staff Software Engineer - Managed Kubernetes role.

Rezi rewrites your resume against Lambda's job description. Free.

Resume score gauge reading 58 out of 100

Don't guess if your resume is good enough.

See how it scores against the Staff Software Engineer - Managed Kubernetes posting at Lambda — free, in seconds.

About the Role

Lambda is building the AI Cloud of the future and seeking a Staff Engineer to contribute to the development of our Managed Kubernetes platform, designed for AI workloads on bare metal. This foundational technical leadership role involves shaping the infrastructure for next-generation AI training and inference at scale.

Responsibilities

Drive technical vision for Lambda's Managed Kubernetes bare-metal platform, including control plane scalability, multi-tenancy, cluster lifecycle management, and high availability.
Integrate and extend NVIDIA's open-source ecosystem: GPU Operator, Network Operator, DCGM, NCCL, and emerging projects like AICR and Topograph for topology-aware scheduling and placement.
Design GPU-aware orchestration systems.
Lead development of services that power our managed services.
Inform on and help with networking solutions for AI workloads: CNI integration (Cilium, Multus), high-performance fabrics (InfiniBand, RoCE), RDMA, and GPUDirect.
Inform and help with storage architecture requirements for AI workloads.
Build the foundation for Managed Slurm on Kubernetes, enabling traditional HPC workloads to run seamlessly alongside Kubernetes workloads.
Design higher-level platform services for inference, including model serving infrastructure, autoscaling based on inference load, and multi-model deployment patterns.
Design self-healing systems and automation for incident response, root cause analysis, and platform resilience.
Lead chaos engineering efforts to validate system behavior under failure conditions at scale.
Establish operational excellence for a managed service: upgrade automation, security patching, and zero-downtime maintenance.
Serve as the technical bridge between Orchestration and other infrastructure teams (Network, Storage, Security), translating platform requirements into actionable specifications.
Drive infrastructure-wide decisions that enable successful managed services.
Provide input on bare-metal provisioning, network topology, and storage systems to ensure they meet the needs of managed services.
Champion consistency and standardization across Lambda's infrastructure stack.
Work directly with customers and internal teams to understand existing deployments and chart a path to the managed platform.
Set technical direction for Kubernetes services across the Orchestration team, influencing roadmap and prioritization.
Drive reviews and design sessions, ensuring systems are scalable, maintainable, and aligned with customer needs.
Mentor and grow engineers, establishing best practices for Kubernetes development, distributed systems, and Cloud Native engineering.
Collaborate cross-functionally with Network, Storage, Security, and Customer Success teams.
Engage with NVIDIA and the open-source community to stay current on GPU orchestration technologies and contribute back.
Represent Lambda externally through technical blog posts, conference talks, and strategic customer engagements.
Shape our AIOps vision: design intelligent systems for automated capacity planning, anomaly detection, and predictive maintenance of cloud infrastructure.

Requirements

10+ years of experience in software engineering, platform engineering, or SRE, with at least 5 years focused on Kubernetes at scale.
Expert-level understanding of Kubernetes internals: API machinery, controllers, schedulers, operators, CRDs, CSI, CNI, and extension patterns.
Holistic infrastructure expertise across compute, networking, storage, and security.
Strong software engineering skills in Go and Python; ability to write production-quality code.
Deep experience with GPU orchestration in Kubernetes: NVIDIA GPU Operator, device plugins, DCGM, MIG, time-slicing, and GPU-aware scheduling.
Proven track record of technical leadership: driving design decisions, mentoring engineers, and influencing infrastructure direction.
Deep experience designing and operating managed services or multi-tenant platforms.
Strong understanding of distributed systems principles: consensus, fault tolerance, consistency models, and graceful degradation.
Experience with observability at scale: Prometheus, Grafana, distributed tracing, and building actionable alerting systems.
Solid knowledge of Linux systems and networking (L2-L7), including high-performance networking concepts (RDMA, InfiniBand, RoCE).
Experience with infrastructure-as-code and GitOps workflows.
Familiarity with NVIDIA Network Operator and GPUDirect is strongly preferred.
Experience building and operating managed Kubernetes services (GKE, EKS, AKS, or similar) or working on Kubernetes control plane components.
Hands-on experience with NVIDIA's open-source ecosystem beyond GPU Operator: Network Operator, NCCL tuning, Topograph, AICR, or similar emerging projects.
Familiarity with HPC and traditional job schedulers (Slurm) and Kubernetes-native batch scheduling (KAI, Volcano, Kueue).
Background in confidential computing.
Experience migrating customers or workloads from legacy/bespoke infrastructure to standardized platforms.
Contributions to CNCF projects, Kubernetes SIGs, or NVIDIA open-source projects.
Familiarity with security and compliance in multi-tenant environments: RBAC, Pod Security Standards, network policies, workload isolation.
Background in ML infrastructure: training clusters, inference serving, simulation.

Skills

Kubernetes
Go
Python
NVIDIA GPU Operator
Device plugins
DCGM
MIG
Time-slicing
GPU-aware scheduling
Distributed systems
Cloud Native
Prometheus
Grafana
Distributed tracing
Linux systems
Networking (L2-L7)
RDMA
InfiniBand
RoCE
Infrastructure-as-code
GitOps
NVIDIA Network Operator
GPUDirect
Managed Kubernetes services
Kubernetes control plane components
HPC
Slurm
Kubernetes-native batch scheduling
Confidential computing
ML infrastructure

Location

San Francisco
San Jose
Bellevue

Work Type

Hybrid

Experience Level

Staff Engineer
10+ years of experience
5+ years focused on Kubernetes at scale

Benefits

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

About the Company

Lambda, The Superintelligence Cloud, is a leader in AI cloud infrastructure serving tens of thousands of customers, ranging 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.
Lambda is building the essential infrastructure for the AI era, founded by ML practitioners, for ML practitioners.
Customers include leading AI research labs and enterprises pushing the boundaries of what's possible with artificial intelligence.
Founded in 2012, with 500+ employees, and growing fast.
Investors 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.
Have research papers accepted at top machine learning and graphics conferences, including NeurIPS, ICCV, SIGGRAPH, and TOG.
Values are publicly available: https://lambda.ai/careers.

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.