Job description

About Maven Maven Robotics designs, builds, and deploys intelligent robotics for logistics and manufacturing. Maven is building the first general-purpose robotics system for industrial work. Our mission is to activate autonomous labor at scale and build toward infinitely elastic industrial capacity. We’re a team of engineers, researchers, and operators who have spent our careers working on the hardest problems in AI, robotics, engineering, and autonomous systems. We work at the intersection of advanced research and real-world engineering, turning ideas into prototypes and deployed systems. At Maven, we build around the realities of industrial work. The robots are designed from first principles for complex physical environments and rigorous commercial standards. They master real tasks that deliver immediate impact, industrial-grade reliability, and intelligence that compounds at scale. We’re growing an ambitious team of people who think in systems, move quickly, and are builders at heart. About the Role As Head of Data Operations at Maven Robotics, you will own the strategy, systems, and teams that turn raw robot experience into high-quality training data. You will lead data collection and evaluation operations, define how research priorities translate into data programs, and ensure our models have the right data to improve reliability, generalization, and performance. You will work closely with robot learning, evaluation, infrastructure, hardware, and data teams to translate model failures and research priorities into scalable collection programs, robust data pipelines, and training-ready datasets. You will also own Maven’s external data strategy, helping determine when we should collect, acquire, license, partner for, or generate data. What You’ll Do Own Maven’s end-to-end data strategy and operations, spanning data collection, evaluation operations, data quality, dataset development, and delivery of training-ready datasets Lead the teams responsible for data collection and evaluation operations, including staffing, processes, quality standards, throughput, and performance Translate research priorities, evaluation results, and model failures into clear data requirements, collection strategies, and dataset roadmaps Partner with research and infrastructure teams to build scalable pipelines from raw robot data through validation, processing, curation, and training dataset generation Define data quality standards and build systems for measuring coverage, diversity, throughput, utilization, cost, and dataset effectiveness Own external data partnerships and evaluate opportunities to collect, acquire, license, partner for, or generate data that accelerates model development Build operational processes and tooling that make data programs reproducible, measurable, and scalable as Maven’s fleet and research organization grow What We’re Looking For Experience building and scaling data, operations, or technical programs in robotics, autonomous systems, machine learning, or another data-intensive technical environment Experience leading teams and cross-functional programs spanning data collection, evaluation, or dataset development Strong technical fluency with machine learning data pipelines, including ingestion, processing, validation, curation, and training dataset generation Experience using model evaluation, failure analysis, or research outcomes to inform data collection and dataset priorities Strong analytical ability to reason about data quality, coverage, throughput, cost, utilization, and operational bottlenecks Ability to work closely with researchers and engineers and translate ambiguous research needs into concrete, scalable operational programs Strong ownership and a track record of independently defining and executing strategy in a fast-moving environment Nice to Have Experience negotiating or managing external data, technology, or research partnerships Experience designing dataset versioning, lineage, governance, or provenance systems for machine learning Experience building internal tools for data collection, QA, dataset management, evaluation, or operational dashboards Familiarity with imitation learning, reinforcement learning, multimodal models, world models, or robot foundation models Experience with synthetic data, simulation-generated data, third-party datasets, or hybrid data strategies Equal Opportunity Maven Robotics is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

Requirements

Must-haves

What You’ll Do
Own Maven’s end-to-end data strategy and operations, spanning data collection, evaluation operations, data quality, dataset development, and delivery of training-ready datasets
Lead the teams responsible for data collection and evaluation operations, including staffing, processes, quality standards, throughput, and performance
Translate research priorities, evaluation results, and model failures into clear data requirements, collection strategies, and dataset roadmaps
Partner with research and infrastructure teams to build scalable pipelines from raw robot data through validation, processing, curation, and training dataset generation
Define data quality standards and build systems for measuring coverage, diversity, throughput, utilization, cost, and dataset effectiveness
Own external data partnerships and evaluate opportunities to collect, acquire, license, partner for, or generate data that accelerates model development
Build operational processes and tooling that make data programs reproducible, measurable, and scalable as Maven’s fleet and research organization grow
What We’re Looking For
Experience building and scaling data, operations, or technical programs in robotics, autonomous systems, machine learning, or another data-intensive technical environment
Experience leading teams and cross-functional programs spanning data collection, evaluation, or dataset development
Strong technical fluency with machine learning data pipelines, including ingestion, processing, validation, curation, and training dataset generation
Experience using model evaluation, failure analysis, or research outcomes to inform data collection and dataset priorities
Strong analytical ability to reason about data quality, coverage, throughput, cost, utilization, and operational bottlenecks
Ability to work closely with researchers and engineers and translate ambiguous research needs into concrete, scalable operational programs
Strong ownership and a track record of independently defining and executing strategy in a fast-moving environment

Nice-to-haves

Experience negotiating or managing external data, technology, or research partnerships
Experience designing dataset versioning, lineage, governance, or provenance systems for machine learning
Experience building internal tools for data collection, QA, dataset management, evaluation, or operational dashboards
Familiarity with imitation learning, reinforcement learning, multimodal models, world models, or robot foundation models
Experience with synthetic data, simulation-generated data, third-party datasets, or hybrid data strategies

Application prep

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Keywords to mirror

aimachine learning

Platform: greenhouse
Effort: quick