This role pays less than
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of similar roles. Most pay
$191,300–$255,925
— the shaded band above.
At the midpoint, this role pays about
$213k
versus about
$224k
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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
899 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 886 roles with salary data.
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 manage the customer-facing operational lifecycle of large-scale automotive data transformed into versioned datasets for training and evaluating machine learning models across the autonomous-driving stack. Responsibilities include capturing release requirements, coordinating execution with engineering teams, monitoring production workflows, identifying risks, and validating results against quality criteria. You will produce release notes and documentation while ensuring clear communication regarding status and incidents. The role requires expertise in the machine learning data lifecycle, including curation, labeling, and versioning. Required skills include SQL, data-analysis tools, Python, Databricks, and experience with automotive sensor and ground-truth data such as lidar, radar, and camera systems to solve complex problems in autonomous vehicle development.
Serve as the primary operational partner for ML engineers to capture and clarify dataset release requirements.
Manage the release calendar by coordinating priorities, dependencies, engineering readiness, and compute capacity across multiple tracks.
Monitor production release workflows from launch through delivery to identify failures, resource constraints, and risks.
Coordinate with engineering and infrastructure teams to resolve technical issues and bottlenecks in the data pipeline.
Validate release results against volume, signal, version, and quality criteria before notifying customers of availability.
Provide consistent communication to customers regarding release status, risk assessments, incident reports, and recovery plans.
Produce comprehensive release notes, delivery announcements, and documentation for ML teams to use datasets confidently.
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 other data-intensive roles.
Solid understanding of the machine learning data lifecycle including collection, curation, labeling, validation, versioning, and distribution.
Ability to use SQL and data-analysis tools to investigate dataset contents and identify quality issues.
Strong communication skills to translate requirements between ML engineers, data specialists, and infrastructure teams.
Proven track record of influencing without direct authority in a matrixed organization.
Experience with large-scale dataset workflows for autonomous driving, ADAS, robotics, or computer vision (preferred).
Familiarity with automotive sensor data, Python, Databricks, or automation tools to improve operational workflows (preferred).
Autonomous Vehicles
Machine Learning
ML ops
Data Ops
ADAS
L4 Robotaxi
Software Development
Product Roadmap
Validation
Data Collection
KPIs
Vehicle Systems
Reinforcement Learning
Python
C++
Deep Learning
GPU Clusters
Kubernetes
SLURM
High-Performance Computing
Data Pipelines
Simulation
Closed-Loop Simulation
PPO
GRPO
NuRec
Cosmos World Model