ML Data Operations Lead: Dataset Release & Validation

NVIDIA; Santa Clara (CA); On-site; USD 168,000 - 322,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA is seeking an ML Data Operations Lead to own the customer-facing operational lifecycle of AV datasets. You will partner with ML engineers to translate data needs into release requirements, coordinate with the engineering team, and ensure every release has clear validation, documentation, and communication.

This senior individual-contributor role requires deep technical depth to investigate problems, assess delivery risk, and manage priorities in a fast-moving environment where data

Qualifications

• 6+ years in ML data operations or data-intensive operational roles.
• Understanding of ML data lifecycle: collection, curation, labeling, validation, versioning, release, storage, consumption.

Responsibilities

• Serve as the primary operational partner for ML engineers and internal consumers of AV datasets.
• Capture and clarify dataset release requirements including signals, labels, data volumes, cadence, and acceptance criteria.
• Manage release calendar and coordinate priorities across multiple dataset-release tracks.
• Monitor production release workflows and drive resolution with engineers and infrastructure owners.
• Validate release results against volumes, signals, versions, and quality criteria; communicate status to customers.
• Produce release notes, delivery announcements, and handoff information for ML teams.

Skills

ML data operations

SQL

Cross-functional collaboration

Communication skills

Customer orientation

Problem solving

Education

Bachelor’s degree in CS/Engineering/Data Science

Tools

Python

Databricks

Job description

NVIDIA is seeking an ML Data Operations Lead to own the customer-facing operational lifecycle of AV datasets. You will partner with ML engineers to translate data needs into release requirements, coordinate with the engineering team, and ensure every release has clear validation, documentation, and communication.

This senior individual-contributor role requires deep technical depth to investigate problems, assess delivery risk, and manage priorities in a fast-moving environment where data