ML Data Operations Lead, Dataset Release and Delivery - Autonomous Vehicles
$168k - $322k • Remote • US, CA, Santa Clara
Posted 1mo ago
Job Location
US, CA, Santa Clara
Tech Stack
Remote Work Policy
Fully remote
Categories
Machine Learning Engineer
About the job
NVIDIA is seeking an ML Data Operations Lead to manage the operational lifecycle of datasets for autonomous vehicle development. This role involves partnering with ML engineers to understand their data needs, translating them into release requirements, coordinating execution with the engineering team, and ensuring reliable, transparent, and scalable delivery of versioned datasets. You will focus on operational ownership, investigating problems, assessing delivery risks, and ensuring clear validation, documentation, and communication for each release, rather than developing data pipelines.
Responsibilities
- Serve as the primary operational partner for ML engineers and internal consumers of AV datasets.
- Capture and clarify dataset release requirements, including use cases, signals, labels, volumes, cadence, timelines, storage, and acceptance criteria.
- Manage the release calendar, coordinating priorities, dependencies, engineering readiness, and compute capacity across multiple release tracks.
- Monitor production release workflows, identify risks and failures, and drive resolution with relevant engineering and infrastructure teams.
- Validate release results against expected volumes, signals, versions, and quality criteria before communicating availability.
- Maintain timely and accurate communication with customers regarding release status, risks, incidents, and recovery plans.
- Produce release notes, delivery announcements, known-issue documentation, and handoff information to facilitate dataset understanding and usage.
Requirements
- Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or equivalent experience.
- 6+ years of experience in ML data operations, technical service delivery, dataset operations, release operations, technical program execution, or a data-intensive operational role.
- Solid understanding of the machine learning data lifecycle (collection, curation, labeling, validation, versioning, release, storage, consumption).
- Ability to use SQL and data-analysis tools to investigate dataset contents and identify quality issues.
- Strong customer orientation and skill in translating between ML engineers, data specialists, and infrastructure teams.
- Excellent written communication skills for producing detailed requirements, release notes, status updates, and operating procedures.
- Excellent judgment in balancing customer timelines, engineering capacity, system reliability, data quality, and competing priorities.
- Proven track record of influencing without direct authority and driving work to completion in a matrixed organization.
- Comfort operating in a fast-moving environment with changing requirements and constraints.
Benefits
- Equity
- Benefits