> Markdown version of [/jobs/ext/3078101-manager-of-perception-data](https://www.wearedevelopers.com/jobs/ext/3078101-manager-of-perception-data). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Manager of Perception Data

- **Company:** Zoox
- **Location:** Foster City, CA, United States
- **Experience:** Expert
- **Salary:** $339,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Computer Vision, Big Data, Data Discovery, Information Engineering, Machine Learning, Search Technologies, Model-Driven Development, Data Selection
- **Published:** September 25, 2026
- **Apply:** https://jobs.lever.co/zoox/354121d1-018e-43fa-87a3-f29baf107d6f/apply

## About the Role

* 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.

## Description

* 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.


## Related Videos

- [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology)
- [Intelligent Data Selection for Continual Learning of AI Functions](https://www.wearedevelopers.com/videos/367-intelligent-data-selection-for-continual-learning-of-ai-functions)
- [Building Agentic Applications: A Deep Dive into Zooka, an AI Cardiologist Assistant](https://www.wearedevelopers.com/videos/2030-building-agentic-applications-a-deep-dive-into-zooka-an-ai-cardiologist-assistant)
- [3 Ways to Rebuild the Data Stack for Agents](https://www.wearedevelopers.com/videos/100091-3-ways-to-rebuild-the-data-stack-for-agents)
- [Finding the unknown unknowns: intelligent data collection for autonomous driving development](https://www.wearedevelopers.com/videos/519-finding-the-unknown-unknowns-intelligent-data-collection-for-autonomous-driving-development)
- [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data)

## Related Articles

- [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)
- [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)
- [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering)
- [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)
- [Everything a Developer Needs to Know About MCP with Neo4j](https://www.wearedevelopers.com/magazine/604-everything-a-developer-needs-to-know-about-mcp-with-neo4j)
- [The future is here: How artificial intelligence is influencing UX design](https://www.wearedevelopers.com/magazine/65-the-future-is-here-how-artificial-intelligence-is-influencing-ux-design)