below marketmost similar roles pay hereabove market
This listing doesn't post a salary. Most similar roles pay
$196,750–$254,750.
Based on 240 similar postings.
Employer
About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
1814 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 1204 roles with salary data.
The ML Data Operations Lead, Dataset Release and Delivery - Autonomous Vehicles is a senior individual-contributor role within the AV MLOps Dataset Release team. You will own the customer-facing operational lifecycle of transforming large-scale automotive data into versioned, trustworthy datasets used to train and evaluate machine learning models across the autonomous-driving stack. Daily responsibilities include capturing dataset requirements, coordinating release calendars, monitoring production workflows, and validating results against quality criteria. You will serve as the primary operational partner for ML engineers, translating needs into actionable requirements while managing risks and dependencies. Required skills include a deep understanding of the machine learning data lifecycle, SQL, and data-analysis tools. Experience with Python, Databricks, and automotive sensor data like lidar or radar is preferred to solve complex data delivery problems.
Capture and clarify dataset release requirements including use cases, data volumes, and acceptance criteria from ML engineers.
Manage the release calendar by coordinating priorities, dependencies, engineering readiness, and compute capacity across multiple tracks.
Monitor production release workflows to identify and resolve failures, resource constraints, and missing data.
Validate release results against expected volumes, signals, versions, and quality criteria before customer delivery.
Provide timely status updates, incident summaries, and recovery plans to internal customers regarding release progress.
Produce release notes, delivery announcements, and known-issue documentation to enable ML teams to use datasets.
Use SQL and data-analysis tools to investigate dataset contents and reconcile expected versus delivered results.
What we're looking for
Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or a related field, or equivalent experience.
6+ years of experience in ML data operations, technical service delivery, dataset operations, release operations, technical program execution, or another data-intensive operational role.
Solid understanding of the machine learning data lifecycle, including collection, curation, labeling, validation, versioning, release, storage, and consumption.
Ability to use SQL and data-analysis tools to investigate dataset contents and identify quality or completeness issues.
Strong skill in translating requirements between ML engineers, data specialists, infrastructure teams, and other technical collaborators.
Excellent written communication skills to produce detailed requirements, release notes, status updates, and operating procedures.
Experience operating large-scale dataset generation, materialization, validation, or delivery workflows for autonomous-driving, ADAS, robotics, or computer-vision systems (preferred).
Hands-on experience with Python, notebooks, Databricks, dashboards, or lightweight automation to investigate data and improve workflows (preferred).
Snowflake
dbt
SQL
Power BI
Azure DevOps
Jira
Data Governance
Metadata Management
Secoda
AI
cloud data platform s
Data Engineering
Analytics Engineering