About this role
In this role, you will...
- Own the perception data flywheel. Run the learning loop from real world fleet signals and model behavior through data discovery, curation, and enrichment into training, evaluation, and measurement.
- Lead multidisciplinary teams. Manage and develop a 10+ person team of data science, data engineering and data labeling, setting priorities and raising the bar on execution.
- Automate annotation at scale. Drive auto annotation and auto labeling pipelines, reducing manual cost while improving label quality and throughput.
- Build advanced data miners. Develop intelligent approaches to surfacing rare, surprising, and safety critical scenarios in very large datasets, using techniques such as embeddings, semantic search, learned representations, and model driven data selection.
- Own log selection and storage. Define how we select, store, and retrieve fleet logs so the right data reaches our models efficiently and economically.
- Connect data to model performance. Establish how we measure the value of data and make rigorous trade offs across quality, accuracy, speed, cost, and scale.
- Close the loop. Feed curated data into model training and evaluation, then analyze outcomes to continuously refine what we collect and label.
- Partner cross functionally. Work closely with ML, metrics and evaluation, and infrastructure teams to keep the flywheel running reliably at scale.
Qualifications
Qualifications
- Track record of building and leading high performing technical teams (data science, data engineering, ML, or labeling), including managing managers or senior individual contributors.
- Deep technical expertise in computer vision, video, multimodal AI, or related perception problems.
- Experience leading large scale data capabilities that directly influence model training, evaluation, and performance.
- Strong intuition for what makes data valuable, with experience in data discovery, selection, curation, and enrichment at scale.
- Proven execution at scale, with strong technical and commercial judgment across quality, speed, cost, and build versus buy trade offs.
- Ability to move between strategy and technical detail, set direction in ambiguity, and influence senior technical and business stakeholders.
- Bonus :
Autonomous driving, robotics, or embodied AI.
- Large scale video, multimodal, or foundation model training.
- Semantic search, embeddings, active learning, auto labeling, or other approaches to intelligent data selection and enrichment.
- Large scale real world data acquisition across fleets, partners, or multiple geographies.
This outlined range is for base salary.
There are three major components to compensation for this position: base salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights. A sign-on bonus may be offered as part of the compensation package. The listed range applies only to the base salary. Compensation will vary based on geographic location and level. Leveling, as well as positioning within a level, is determined by a range of factors, including, but not limited to, a candidate's relevant years of experience, domain knowledge, and interview performance. The salary range listed in this posting is representative of the range of levels Zoox is considering for this position.
Zoox also offers a comprehensive package of benefits, including paid time off (e.g. sick leave, vacation, bereavement), unpaid time off, Zoox Stock Appreciation Rights, Amazon RSUs, health insurance, long-term care insurance, long-term and short-term disability insurance, and life insurance.