Technical Program Manager, Dataset Operations

NVIDIA•Santa Clara, CA

2d•$168,000 - $322,000

About The Position

We are looking for a Technical Program Manager (TPM) to lead the end-to-end management of large-scale, multi-modal datasets that power next-generation Physical AI systems. This role will focus heavily on vendor-sourced data pipelines, ensuring data quality, compliance, scalability, and alignment with research and product goals. You will operate at the intersection of research, engineering, data vendors, and legal/compliance, driving execution across complex, high-stakes data programs.

Requirements

5+ years of experience as a TPM, Product Ops, or similar role in AI/ML or data-intensive systems
Masters degree (or equivalent experience)
Proven experience managing external vendors or large-scale data programs
Strong understanding of ML data pipelines and dataset lifecycle
Ability to operate in ambiguous, fast-moving research environments
Familiarity with one or more: Computer vision / video datasets, Robotics / embodied AI data, Autonomous driving datasets
Understanding of: Annotation workflows, Data quality evaluation, Dataset biases and coverage challenges
Strong program structuring skills (clear specs, milestones, tracking)
Ability to convert high-level goals into concrete execution plans
Comfortable working across research, engineering, and external partner

Nice To Haves

Experience with multi-modal datasets (video, sensor, action data)
Background in robotics, self-driving, or AR/VR
Experience building vendor ecosystems from scratch
Familiarity with data compliance (GDPR, privacy, licensing constraints)

Responsibilities

Own the lifecycle of large-scale datasets across modalities: Ego-centric video (AR/VR, human interaction), Robotics data (manipulation, embodied AI), Autonomous driving data (multi-sensor, multi-agent)
Manage external data vendors end-to-end: Scope definition, onboarding, and contracting, Data specification and annotation guidelines, Delivery tracking, quality control, and iteration loops
Establish scalable processes for multi-vendor coordination
Translate research and model requirements into clear, enforceable data specs
Define and track data quality metrics (coverage, diversity, labeling accuracy, temporal consistency)
Drive continuous improvement via structured feedback loops with vendors
Partner with Research teams (to understand evolving model needs)
Partner with Engineering teams (data pipelines, storage, tooling)
Partner with Legal/compliance (data usage rights, privacy, licensing)
Align dataset strategy with model training and product timelines
Build frameworks for: Dataset versioning and traceability, Vendor performance benchmarking, Cost vs. quality optimization
Drive automation where possible, but maintain strong operational rigor