{"id":1206637,"url":"https://alion.io/job/lambda-staff-software-engineer-managed-kubernetes-2","title":"Staff Software Engineer - Managed Kubernetes","company":{"id":32991,"name":"Lambda","domain":"lambda.ai","url":"https://alion.io/company/lambda-ai","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":null},"role":"Backend","role_family":"Backend","seniority":"staff","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":314000,"max":465000,"currency":"USD","period":"year","gross":null,"usd_annual":465000},"salary_estimate":null,"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"AIOps","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"AWS Lambda","optional":false},{"name":"Chaos Engineering","optional":false},{"name":"Cilium","optional":false},{"name":"Claude Code","optional":false},{"name":"GitOps","optional":false},{"name":"Google GKE","optional":false},{"name":"Grafana","optional":false},{"name":"HPC","optional":false},{"name":"InfiniBand","optional":false},{"name":"Kubernetes","optional":false},{"name":"Linux","optional":false},{"name":"NCCL","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Prometheus","optional":false},{"name":"Python","optional":false},{"name":"Self-Healing","optional":false},{"name":"SLURM","optional":false},{"name":"Amazon EKS","optional":true},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"Azure AKS","optional":true},{"name":"GCP","optional":true},{"name":"Machine Learning","optional":true}],"status":"live","first_seen_at":"2026-09-24T22:55:14Z","employer_posted_date":"2026-09-24","last_verified_at":"2026-09-26T02:11:35Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"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.\nIf you'd like to build the world's best AI cloud, join us.\n*Note: This position requires presence in our San Francisco, San Jose, or Bellevue office location 4 days per week; Lambda’s designated work from home day is currently Tuesday.\nAbout the Role\nLambda is building the AI Cloud of the future. We are seeking a Staff Engineer to help our development of our Managed Kubernetes platform. Think GKE, but purpose-built for AI workloads and running on bare metal. This is a foundational technical leadership role where you will shape the infrastructure that powers the next generation of AI training and inference at scale.\nAs a Staff Engineer on our Orchestration team, you will collaborate to help drive the technical vision for Lambda's managed orchestration services, including Managed Kubernetes, Managed Slurm on Kubernetes, and higher-level platform services for inference and AIOps. You'll work at the intersection of distributed systems, GPU-accelerated computing, and Cloud Native infrastructure to build systems that are reliable, performant, and elegantly simple for our customers.\nThis is not a role for someone who just operates Kubernetes; it is a technical leadership role for an engineer who has synthesized the core domains of infrastructure (compute, network, storage, security) and can design holistic solutions across all of them. You'll be working closely with NVIDIA's open-source ecosystem, and partnering with internal teams across the stack to deliver a world-class managed platform.\nWhat You'll Do\nProduct Engineering\nDrive technical vision for Lambda's Managed Kubernetes bare-metal platform, including control plane scalability, multi-tenancy, cluster lifecycle management, and high availability\n\nIntegrate 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\n\nDesign GPU-aware orchestration systems\n\nLead development of services that power our managed services\n\nInform on and help with networking solutions for AI workloads: CNI integration (Cilium, Multus), high-performance fabrics (InfiniBand, RoCE), RDMA, and GPUDirect. You will work closely with our Network team to define and drive requirements\n\nInform and help with storage architecture requirements for AI workloads. You will partner with Storage teams on what managed K8s, Slurm, and future services need\n\nBuild the foundation for Managed Slurm on Kubernetes, enabling traditional HPC workloads to run seamlessly alongside Kubernetes workload\n\nDesign higher-level platform services for inference, including model serving infrastructure, autoscaling based on inference load, and multi-model deployment patterns\n\nDesign self-healing systems and automation for incident response, root cause analysis, and platform resilience\n\nLead chaos engineering efforts to validate system behavior under failure conditions at scale\n\nEstablish operational excellence for a managed service: upgrade automation, security patching, and zero-downtime maintenance\n\nCross-Functional Infrastructure Leadership\nServe as the technical bridge between Orchestration and other infrastructure teams (Network, Storage, Security), translating platform requirements into actionable specifications\n\nDrive infrastructure-wide decisions that enable successful managed services. You’re someone who understands what's needed end-to-end, not just at the Kubernetes layer.\n\nProvide input on bare-metal provisioning, network topology, and storage systems to ensure they meet the needs of managed the services being built by the Orchestration organization\n\nChampion consistency and standardization across Lambda's infrastructure stack\n\nWork directly with customers and internal teams to understand existing deployments and chart a path to the managed platform\n\nTechnical Leadership\nSet technical direction for Kubernetes services across the Orchestration team, influencing roadmap and prioritization\n\nDrive reviews and design sessions, ensuring we build systems that are scalable, maintainable, and aligned with customer needs\n\nMentor and grow engineers, establishing best practices for Kubernetes development, distributed systems, and Cloud Native engineering\n\nCollaborate cross-functionally with Network, Storage, Security, and Customer Success teams\n\nEngage with NVIDIA and the open-source community to stay current on GPU orchestration technologies and contribute back where appropriate\n\nRepresent Lambda externally through technical blog posts, conference talks, and strategic customer engagements\n\nShape our AIOps vision: design intelligent systems for automated capacity planning, anomaly detection, and predictive maintenance of cloud infrastructure\n\nWho You Are\nYou are a creative, innovative engineer who operates at high velocity. You don't just solve problems. You find elegant solutions and ship them quickly. You embrace modern tools and AI-assisted development (like Claude Code) to accelerate your productivity and multiply your impact. You're energized by building new things, not maintaining the status quo.\nRequired Qualifications\n10+ years of experience in software engineering, platform engineering, or SRE, with at least 5 years focused on Kubernetes at scale\n\nExpert-level understanding of Kubernetes internals: API machinery, controllers, schedulers, operators, CRDs, CSI, CNI, and the extension patterns that make Kubernetes powerful\n\nHolistic infrastructure expertise: you've synthesized knowledge across compute, networking, storage, and security, not just Kubernetes in isolation. You can build solutions that span the full stack.\n\nStrong software engineering skills in Go (required) and Python; you write production-quality code, not just scripts\n\nDeep experience with GPU orchestration in Kubernetes: NVIDIA GPU Operator, device plugins, DCGM, MIG, time-slicing, and GPU-aware scheduling. Familiarity with NVIDIA Network Operator and GPUDirect is strongly preferred.\n\nProven track record of technical leadership: driving design decisions across teams, mentoring engineers, and influencing infrastructure direction beyond your immediate scope\n\nDeep experience designing and operating managed services or multi-tenant platforms. You understand what it takes to run infrastructure for external customers\n\nStrong understanding of distributed systems principles: consensus, fault tolerance, consistency models, and graceful degradation\n\nExperience with observability at scale: Prometheus, Grafana, distributed tracing, and building actionable alerting systems\n\nSolid knowledge of Linux systems and networking (L2-L7), including high-performance networking concepts (RDMA, InfiniBand, RoCE)\n\nExperience with infrastructure-as-code and GitOps workflows\n\nPreferred Qualifications\nExperience building and operating managed Kubernetes services (GKE, EKS, AKS, or similar) or working on Kubernetes control plane components\n\nHands-on experience with NVIDIA's open-source ecosystem beyond GPU Operator: Network Operator, NCCL tuning, Topograph, AICR, or similar emerging projects\n\nFamiliarity with HPC and traditional job schedulers (Slurm) and Kubernetes-native batch scheduling (KAI, Volcano, Kueue)\n\nBackground in confidential computing\n\nExperience migrating customers or workloads from legacy/bespoke infrastructure to standardized platforms\n\nContributions to CNCF projects, Kubernetes SIGs, or NVIDIA open-source projects\n\nFamiliarity with security and compliance in multi-tenant environments: RBAC, Pod Security Standards, network policies, workload isolation\n\nBackground in ML infrastructure: training clusters, inference serving, simulation\n\nWhy Lambda\nLambda is building the essential infrastructure for the AI era. We're not just another cloud provider: we're a company founded by ML practitioners, for ML practitioners. Our customers include leading AI research labs and enterprises pushing the boundaries of what's possible with artificial intelligence.\nWhat makes this role special:\nYou'll be building core platform services the world’s largest AI companies will consume\n\nNVIDIA partnership: Deep integration with NVIDIA's GPU and networking stack, working with cutting-edge open-source tooling\n\nReal technical challenges: Massive scale GPU clusters and the unique demands of AI workloads\n\nCross-stack influence: Shape not just Kubernetes, but the network, storage, and compute infrastructure that supports it\n\nDirect impact: Your work enables AI breakthroughs. Every model trained on Lambda benefits from systems you build\n\nWorld-class team: Work alongside engineers with deep expertise in ML, systems, and infrastructure\n\nSalary Range Information\nThe 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.\nAbout Lambda\nFounded in 2012, with 500+ employees, and growing fast\n\nOur 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\n\nWe have research papers accepted at top machine learning and graphics conferences, including NeurIPS, ICCV, SIGGRAPH, and TOG\n\nOur values are publicly available: https://lambda.ai/careers\n\nWe offer generous cash & equity compensation\n\nHealth, dental, and vision coverage for you and your dependents\n\nWellness and commuter stipends for select roles\n\n401k Plan with 2% company match (USA employees)\n\nFlexible paid time off plan that we all actually use\n\nEqual Opportunity Employer\nLambda 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.","description_format":"text","description_chars":10404,"description_truncated":false,"requirements":{"experience_years_min":10,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["401k plan","Equity"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial Intelligence","Cloud Platforms (IaaS & PaaS)","Servers & Data Center Hardware","AI Compute & Inference"],"lifecycle":[{"event":"open","at":"2026-09-25T00:24:20Z"}],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.903,"p_room":1,"age_days":0,"expected_fill_days":44,"reasons":["conf:0","velocity","win:early"],"computed_at":"2026-09-25T05:45:01Z"},"pay":{"stated_usd_annual":465000,"is_top_pay":true},"html_url":"https://alion.io/job/lambda-staff-software-engineer-managed-kubernetes-2","json_url":"https://alion.io/job/lambda-staff-software-engineer-managed-kubernetes-2.json","meta":{"generated_at":"2026-09-26T03:59:35Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":4313,"day_limit":5000,"remaining_today":687,"minute_limit":60,"resets_at":"2026-09-27T00:00:00Z"}}}