Staff Technical Program Manager, AI Infrastructure

Job Description

About the RoleWe are seeking a Staff Technical Program Manager (TPM) to leadAVMLInfrastructureprograms for our autonomous driving platform. In this role, you will drive strategy and execution for large-scale ML infrastructure — including training pipelines, model lifecycle management, compute orchestration, and operational reliability — that power next-generation autonomy models. You willoperateat the intersection of ML engineering, platform infrastructure, and operations, ensuring our ML systems are scalable, efficient, and production-ready to support end-to-end model development at scale.---Key ResponsibilitiesProgram LeadershipLead end-to-end strategic planning and execution forAIML Infrastructure programs, delivering measurable improvements in training throughput, platform reliability, and model development velocity. Establish clear programobjectives, milestones, and success metrics to drive predictable, high-quality delivery across multiple engineering and operations teams.Cross-Functional AlignmentCollaborate withAIML engineering, platform, validation, and product teams to define requirements, prioritize initiatives, and deliver solutions that improve AI development cycle performance and operational efficiency.Technical Road mappingTranslate complexMLOpsneeds — from distributed training orchestration to compute resource management and pipeline scaling — into actionable multi-team execution plans with defined owners and measurable outcomes. Align long-term technical roadmaps with organizational goals, ensuring ML infrastructure evolves to support increasing model complexity, dataset scale, and training workloads.Risk & Change ManagementIdentify technical, operational, and program risks early; develop mitigation strategies that protect training timelines, platform stability, and service reliability.Scalability & PerformanceEnsureAIML operations processes and infrastructure are designed for long-term scalability, performance, and operational excellence — including monitoring, incident response, and capacity planning.Metrics & VisibilityDefine KPIs for ML platform performance, training system reliability, model training cycle time, and delivery velocity;maintaintransparent dashboards and executive-ready reporting. Provide leadership with clear insights into progress, tradeoffs, and program health to supporttimelydecision-making.---Required Qualifications:- 10+ years of technical program management experience, including leadership of large, complex, multi-disciplinary programs.- 5+ years working in ML Operations, ML infrastructure, AI platform engineering, or distributedcomputeenvironments.- BS or MS in Engineering, Computer Science, or a related technical field.- Experience supporting large-scale machine learning training or AI infrastructure programs, including compute orchestration, pipeline reliability, and resource management.- Proventrack recordof managing large, complex, cross-functional programs involving infrastructure, software systems, and data pipelines with ambiguous or evolving requirements.- Ability to analyze system performance metrics,identifybottlenecks, and translate insights into program-level improvements.- Exceptional communication, collaboration, and stakeholder management skills.- Deep familiarity with Agile program delivery, task management tools (e.g., Jira), reporting tools, and technical development tooling.Preferred Qualifications:- Experience with GPUcomputemanagement, cluster orchestration (e.g., Kubernetes,Slurm), or cloud infrastructure (GCP, AWS).- Familiarity with ML workflow orchestration tools (e.g., Kubeflow, Airflow, or similar).- Background in SRE, platform engineering, or DevOps practices applied to ML systems.- Experience with observability, SLO/SLI frameworks, and incident management forproductionML platforms.

Relocation: This job is not eligible for relocation benefits. Any relocation costs would be the responsibility of the selected candidate.

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