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

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.