Top Tech Jobs & Startup Jobs

Lead development and enforcement of Physical Infrastructure Cabling Standards across global data centers; author BoDs, SOPs, and MOPs; manage vendors and BOMs; support NPI and deployments; perform site assessments, QC/QA, and drive process improvements to optimize connectivity for GPU cluster deployments.

Own and evolve Lambda’s cloud foundations, including AWS accounts, IAM, networking, DNS, Terraform, Atlantis, governance, compliance automation, and cost visibility. Build self-service infrastructure platforms and tooling in Go or Python, establish reliable operating practices, participate in on-call, and help define the new team’s scope and service boundaries.

Lead the implementation and ongoing administration of Workday Payroll, Time Tracking, and Absence. Configure US and international payroll processes, integrations, tax and compliance controls, payroll accounting, accruals, and time calculations. Partner with Payroll Operations, People, Finance, and Engineering on testing, reconciliations, audits, releases, documentation, training, and troubleshooting. Own payroll readiness, controls, and continuous improvement across a rapidly growing global workforce.

Lead quarterly and annual tax provisions under ASC 740, manage tax accounting close processes, oversee domestic and international filings, and handle tax audits and notices. Perform R&D tax credit, Section 174, Section 382, forecasting, cash tax, transfer pricing, and tax planning analyses. Collaborate across the business to improve tax systems and processes while communicating tax matters to leadership.

Design and build Lambda’s managed Kubernetes platform for large-scale AI workloads. Responsibilities include developing control-plane services, operators, controllers, cluster lifecycle automation, GPU-aware scheduling, inference platform services, internal deployment tools, observability, and resilient distributed systems. The role involves collaboration across networking, storage, compute, and security teams, production troubleshooting, and participation in an on-call rotation.

Lead the technical vision and development of Lambda’s managed Kubernetes platform for AI workloads. Design scalable, multi-tenant orchestration, GPU-aware scheduling, control planes, managed Slurm, inference services, networking, storage integrations, resilience automation, and operational tooling. Provide cross-functional infrastructure leadership, mentor engineers, guide technical roadmaps, engage customers and open-source communities, and shape AIOps capabilities.

Build and operate distributed systems powering Lambda’s GPU cloud, including APIs, control planes, schedulers, workflows, and operational tooling. Own systems through design, implementation, deployment, observability, on-call, incident response, and continuous improvement. Improve reliability, performance, security, scalability, and operability while collaborating across infrastructure, networking, storage, security, SRE, and product teams. Senior engineers lead complex team-level work; Staff engineers additionally shape cross-team architecture and mentorship.

Leads the architecture, operations, security, capacity, budgets, and reliability of Lambda’s global cloud and AI networks. Builds multi-year network and data center roadmaps, oversees internet peering and backbone strategy, ensures network readiness for AI data center builds, and drives automation, telemetry, and secure-by-design practices. Manages multidisciplinary network and software engineering teams, develops engineering leaders, partners with executives and product teams, and represents technical strategy to customers and partners.

Leads software engineering teams building large-scale network services for an AI cloud. Owns architecture, distributed network systems, security, observability, traffic engineering, reliability, and long-term technical strategy. Hires and develops engineering leaders, establishes operational standards, partners with network and product organizations, and represents technical direction to executives and customers. Requires extensive distributed systems, cloud networking, software engineering, and engineering management experience.

Create and distribute technically accurate resources about deploying production AI workloads on Lambda. Build enterprise AI practitioner reach through technical content, talks, workshops, videos, conferences, podcasts, communities, and partnerships. Collaborate with machine learning and engineering teams on guides, demonstrations, and open-source examples. Gather enterprise deployment feedback and share insights with technical and go-to-market teams to shape Developer Relations priorities and product direction.