ML Data Operations Lead – Dataset Release and Delivery, Autonomous Vehicles

🔥 12 hours ago

🏄 California, Colorado, +2 more states – Remote

🏄 California – Remote ⛰️ Colorado – Remote 🎰 Nevada – Remote 🌲 Oregon – Remote

💵 $168k - $322k / year

⏰ Full Time

🟠 Senior

⚙️ Operations

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👻 Ghost score 1%

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NVIDIA

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1993

🏥 Healthcare

🏭 Manufacturing

🤖 Artificial Intelligence

Healthcare • Manufacturing • Artificial Intelligence

NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.

📋 Description

• 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 • Own 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 coordinate 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 enabling ML teams to understand and use each dataset confidently

🎯 Requirements

• 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

🏖️ Benefits

• Equity • Benefits