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

This is a fully remote position, open to applicants in California, +3 more states.

📋 Description

• Act as the main operational partner for ML engineers and other internal users of AV datasets.

• Gather and clarify dataset release requirements, which include intended use cases, necessary signals and labels, data volumes, release frequency, delivery schedules, storage locations, and acceptance criteria.

• Manage the release calendar while coordinating priorities, dependencies, engineering readiness, and compute capacity across various concurrent dataset-release tracks.

• Oversee production release workflows from the initiation phase to delivery.

• Identify failures, stalled tasks, resource limitations, missing data, and other risks, and collaborate with engineers and infrastructure owners to facilitate resolutions.

• Validate release outcomes against expected volumes, signals, versions, and quality standards before informing customers about availability.

• Ensure timely and accurate communication with customers concerning release status, risks, incidents, changing estimates, and recovery strategies.

• Create release notes, delivery announcements, known-issue documentation, and handoff information to empower ML teams in confidently understanding and utilizing each dataset.

⛳️ Requirements

• A Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or a related discipline, or equivalent experience.

• Over 6 years of experience in ML data operations, technical service delivery, dataset operations, release operations, technical program execution, or another data-intensive operational role.

• Strong understanding of the machine learning data lifecycle, encompassing data collection, curation, labeling, validation, versioning, release, storage, and consumption by training or evaluation pipelines.

• Proficient in using SQL and data-analysis tools to examine dataset contents, reconcile expected with delivered results, and identify quality or completeness issues.

• Strong customer focus and ability to translate between ML engineers, data specialists, infrastructure teams, and other technical collaborators.

• Exceptional written communication skills, including the ability to produce detailed requirements, release notes, status updates, incident summaries, and operational procedures.

• Excellent judgment in balancing customer timelines, engineering capacity, system reliability, data quality, and competing release priorities.

• Proven ability to influence without direct authority and drive projects to completion within a highly matrixed organization.

• Comfortable working in a fast-paced environment where requirements, data availability, and technical constraints may change rapidly.

• Experience managing large-scale dataset generation, materialization, validation, or delivery workflows, particularly 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.

• Practical experience with Python, notebooks, Databricks, dashboards, or lightweight automation used for data investigation and improving operational workflows.

• Experience in defining service-level objectives, operational metrics, alerting, incident management practices, and root-cause corrective actions.

• A history of transforming frequently repeated customer requests or operational challenges into standardized, automated, and scalable services.

🏝️ Benefits

• Equity

• Benefits