In this role, you will:
- Define and implement scalable data quality measures across complex, multimodal data labeling pipelines
- Drive data-centric ML model improvements to achieve critical Zoox milestones
- Support an org-wide data ontology and class structure for perception models
- Determine trade-offs and integrations between human-labeled, human-in-the-loop, and zero-shot autolabeled data
- Build metrics to quantify labeling throughput, capacity, and annotator/vendor quality
Qualifications:
- Master's or PhD degree in a field relevant to autonomous driving (computer science, robotics) to the analysis of human data (computational neuroscience, cognitive science) or a related field
- Proficient using data query languages (SQL and/or Spark/scala) to quickly build complex yet efficient data queries at scale and using Python to build production-quality code
- Proficient in exploratory data analysis (EDA) and data visualization to understand and present trends and their implications for the business.
- Background in statistical modeling and analysis; including experience making data-driven decisions that connect point and uncertainty estimates to business impact.
- Experience with data-centric ML development and data curation
Bonus Qualifications:
- Experience with experiment design and statistical comparisons (A/B testing, parametric/non-parametric statistics, etc.)
- Experience with human data collection, including annotation task design
Top Skills
What We Do
Zoox is an autonomous mobility company that was founded to provide a safer, cleaner, and more enjoyable future on the road. To achieve that goal, the company has spent the past 10 years creating a purpose-built robotaxi that gives the world a better way to ride.
Why Work With Us
At Zoox, we are working to solve one of the greatest technological challenges of our generation. From the beginning, we have been focused on our goal of reimagining transportation from the ground up. We are a mission-driven community of innovators working together to create a safer, cleaner, and more enjoyable future on the road.
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