Staff Software Engineer – Managed Kubernetes

Lambda

. Drive technical vision for Lambda's Managed Kubernetes bare-metal platform .

Posted 9/25/2026full-timeUnited StatesLead💰 $314,000 - $465,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expert-level proficiency in Kubernetes internals and GPU orchestration, with a strong foundation in software engineering and platform management. Capable of leading technical vision, mentoring teams, and driving infrastructure decisions in a multi-tenant environment.

Highest-signal resume keywords

Kubernetes Internals ExpertiseGPU Orchestration in KubernetesTechnical Leadership and MentoringInfrastructure-as-Code and GitOpsObservability at Scale

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Go ProgrammingPython ProgrammingKubernetesDistributed Systems PrinciplesLinux Systems and NetworkingGPU-Aware SchedulingManaged Services DesignChaos EngineeringObservability ToolsHPC and Job Schedulers

Soft Skills

Technical VisionCollaborationMentoringInfluencing Infrastructure DirectionCustomer Engagement

Tools & Technologies

NVIDIA GPU OperatorPrometheusGrafanaDCGMMIGRDMAInfiniBandRoCESlurmCNCF Projects

Industry Keywords

Managed KubernetesMulti-Tenant PlatformsAI WorkloadsIncident-Response AutomationSecurity and ComplianceCapacity PlanningAnomaly DetectionPredictive MaintenanceOpen-Source Community EngagementTechnical Content Creation

Tech Stack

Tools & technologies

Distributed SystemsGrafanaKubernetesLinuxPrometheusPythonGo

About the role

Key responsibilities & impact

Drive technical vision for Lambda's Managed Kubernetes bare-metal platform
Integrate and extend NVIDIA's open-source ecosystem
Design GPU-aware orchestration systems
Lead development of services powering managed services
Define networking requirements for AI workloads, including CNI, high-performance fabrics, RDMA, and GPUDirect
Inform storage architecture requirements for AI workloads
Build the foundation for Managed Slurm on Kubernetes
Design inference platform services, autoscaling, and multi-model deployment patterns
Design self-healing systems and incident-response automation
Lead chaos engineering efforts
Establish operational excellence through upgrade automation, security patching, and zero-downtime maintenance
Bridge Orchestration with Network, Storage, and Security teams
Drive infrastructure-wide decisions and standardization
Work with customers and internal teams on migration to the managed platform
Set technical direction, influence roadmap and prioritization, and lead design reviews
Mentor and grow engineers
Collaborate with Network, Storage, Security, and Customer Success teams
Engage with NVIDIA and the open-source community
Represent Lambda through technical content, conference talks, and customer engagements
Shape AIOps capabilities for capacity planning, anomaly detection, and predictive maintenance

Requirements

What you’ll need

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 (required) and Python
Deep experience with GPU orchestration in Kubernetes, including 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 actionable alerting systems
Solid knowledge of Linux systems and networking (L2-L7), including RDMA, InfiniBand, and RoCE
Experience with infrastructure-as-code and GitOps workflows
Experience building and operating managed Kubernetes services or Kubernetes control plane components
Hands-on experience with NVIDIA's open-source ecosystem beyond GPU Operator
Familiarity with HPC and traditional job schedulers such as Slurm and Kubernetes-native batch scheduling
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
Background in ML infrastructure, including training clusters, inference serving, or simulation
Ability and willingness to work onsite at the San Francisco office 4 days a week
Legal authorization to work in the United States

Benefits

Comp & perks

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
AI-assisted development tools
World-class team and opportunities to work with NVIDIA's GPU and networking stack
Technical blog posts, conference talks, and strategic customer engagement opportunities