Job description

NVIDIA is redefining the automotive industry through accelerated computing, artificial intelligence, simulation, and full-stack autonomous vehicle development. The pace and quality of AV development depend on delivering the right sensor, ground-truth, and derived data to machine learning teams reliably, transparently, and at scale. It's truly the data that makes the cars drive! The AV MLOps Dataset Release team transforms large-scale automotive data into versioned, trustworthy datasets used to train and evaluate machine learning models across the autonomous-driving stack. We are seeking an ML Data Operations Lead to own the customer-facing operational lifecycle of these releases. In this role, you will work at the intersection of machine learning, data engineering, infrastructure, and release operations. You will partner with ML engineers to understand their data needs, translate those needs into actionable release requirements, coordinate execution with the engineering team, and ensure every release is delivered with clear validation, documentation, and communication. This is a senior individual-contributor role. It requires sufficient technical depth to investigate problems, assess delivery risk, and challenge unclear requirements, while focusing primarily on operational ownership rather than developing the underlying data pipelines. What you'll be doing: • Serve as the primary operational partner for ML engineers and other internal consumers of AV datasets. • Capture and clarify dataset release requirements, including intended use cases, required signals and labels, data volumes, release cadence, delivery timelines, storage destinations, and acceptance criteria. • Be responsible for the release calendar and coordinate priorities, dependencies, engineering readiness, and compute capacity across multiple concurrent dataset-release tracks. • Monitor production release workflows from launch through delivery. Identify failures, stalled tasks, resource constraints, missing data, and other risks, then bring together the appropriate engineers and infrastructure owners to drive resolution. • Validate release results against expected volumes, signals, versions, and quality criteria before communicating availability to customers. • Maintain timely, accurate communication with customers regarding release status, risks, incidents, changing estimates, and recovery plans. • Produce release notes, delivery announcements, known-issue documentation, and handoff information that enable ML teams to understand and use each dataset confidently. What we need to see: • 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 data collection, curation, labeling, validation, versioning, release, storage, and consumption by training or evaluation pipelines. • Ability to use SQL and data-analysis tools to investigate dataset contents, reconcile expected and delivered results, and identify quality or completeness issues. • Strong customer orientation and skill in translating between ML engineers, data specialists, infrastructure teams, and other technical collaborators. • Excellent written communication skills, including the ability to produce detailed requirements, release notes, status updates, incident summaries, and operating procedures. • Excellent judgment when balancing customer timelines, engineering capacity, system reliability, data quality, and competing release priorities. • Proven track record of influencing without direct authority and driving work to completion across a highly matrixed organization. • Comfort operating in a fast-moving environment where requirements, data availability, and technical constraints may change quickly. Ways to stand out from the crowd: • Experience operating large-scale dataset generation, materialization, validation, or delivery workflows, especially for autonomous-driving, ADAS, robotics, or computer-vision systems. • Familiarity with automotive sensor and ground-truth data, including camera, lidar, radar, mapping, calibration, or multimodal datasets. • Hands-on experience with Python, notebooks, Databricks, dashboards, or lightweight automation used to investigate data and improve operational workflows. • Experience defining service-level objectives, operational metrics, alerting, incident-management practices, and root-cause corrective actions. • A track record of converting frequently repeated customer requests or operational problems into standardized, automated, and scalable services. NVIDIA brings together some of the most skilled and creative people in technology to solve problems that were once considered impossible. You will have the opportunity to work with teams advancing autonomous vehicles, artificial intelligence, accelerated computing, and large-scale data systems while directly improving the speed and reliability of machine learning development. #AutonomousVehicles Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 168,000 USD - 258,750 USD for Level 4, and 200,000 USD - 322,000 USD for Level 5. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until September 14, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Requirements

Must-haves

Serve as the primary operational partner for ML engineers and other internal consumers of AV datasets.
Capture and clarify dataset release requirements, including intended use cases, required signals and labels, data volumes, release cadence, delivery timelines, storage destinations, and acceptance criteria.
Be responsible for the release calendar and coordinate priorities, dependencies, engineering readiness, and compute capacity across multiple concurrent dataset-release tracks.
Validate release results against expected volumes, signals, versions, and quality criteria before communicating availability to customers.
Maintain timely, accurate communication with customers regarding release status, risks, incidents, changing estimates, and recovery plans.
Produce release notes, delivery announcements, known-issue documentation, and handoff information that enable ML teams to understand and use each dataset confidently.
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 data collection, curation, labeling, validation, versioning, release, storage, and consumption by training or evaluation pipelines.
Ability to use SQL and data-analysis tools to investigate dataset contents, reconcile expected and delivered results, and identify quality or completeness issues.
Strong customer orientation and skill in translating between ML engineers, data specialists, infrastructure teams, and other technical collaborators.
Excellent written communication skills, including the ability to produce detailed requirements, release notes, status updates, incident summaries, and operating procedures.
Excellent judgment when balancing customer timelines, engineering capacity, system reliability, data quality, and competing release priorities.
Proven track record of influencing without direct authority and driving work to completion across a highly matrixed organization.
Comfort operating in a fast-moving environment where requirements, data availability, and technical constraints may change quickly.
Experience operating large-scale dataset generation, materialization, validation, or delivery workflows, especially for autonomous-driving, ADAS, robotics, or computer-vision systems.
Familiarity with automotive sensor and ground-truth data, including camera, lidar, radar, mapping, calibration, or multimodal datasets.
Hands-on experience with Python, notebooks, Databricks, dashboards, or lightweight automation used to investigate data and improve operational workflows.
Experience defining service-level objectives, operational metrics, alerting, incident-management practices, and root-cause corrective actions.
A track record of converting frequently repeated customer requests or operational problems into standardized, automated, and scalable services.
#AutonomousVehicles
You will also be eligible for equity and benefits .
Applications for this job will be accepted at least until September 14, 2026.
This posting is for an existing vacancy.

Nice-to-haves

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Keywords to mirror

machine learningmlsqlpythonai

Platform: eightfold
Effort: medium

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