Own and scale Zoox’s Behavior Evaluation pipelines for autonomous-driving safety metrics. Build reliable PySpark and Databricks data pipelines, implement statistical sampling and projection methods, automate weekly estimates, and develop testing, CI, and data-quality checks. Collaborate with engineering and research stakeholders to apply methodologies consistently, support new releases and geofences, and improve system performance, reproducibility, and auditability.
We are seeking a highly skilled and experienced engineer to join the Behavior Evaluation team and play a pivotal role in measuring the safety of Zoox’s autonomous driving technology. The Behavior Evaluation is the team behind the behavioral part of Zoox’s estimate, quantifying how often the autonomy stack causes the vehicle to stop unexpectedly and how that maps to collision risk. As a member of this team, you will own and scale the pipeline behind these numbers, driving innovation in ML model scoring, sampling, and projections while making the system faster, more reliable, and easier to audit. You will work closely with a team of talented engineers and researchers in a fast paced, collaborative environment as Zoox expands to more cities and software releases.
In this role, you will:
Build and maintain the Behavior Evaluation data pipelines (PySpark/zspark medallion architecture on Databricks, EMR Serverless and Airflow) that produce weekly per-geofence estimates.
Turn the statistical method (sampling, triage weights, mileage sharing, projections) into tested, reproducible code with checks that fail loudly when the data is bad.
Build tools that cut the manual work in each weekly run.
Work with cross functional stakeholders so the methodology is applied consistently in code and in clearance reports.
Help adapt to new release methodologies and new geofences.
Qualifications:
7 - 9 Years of experience or 5+ years with a strong data-platform track record
Expert Python, with production experience in PySpark/Spark and Pandas
Experience designing scalable batch data pipelines: orchestration, Delta/lakehouse tables, idempotent backfills
Solid grounding in applied statistics: sampling, weighting, estimators, confidence intervals
Strong testing and CI habits for data code
Bonus Qualifications:
Experience with autonomous-vehicle simulation, safety metrics or triage workflows
Experience putting ML models into production in a data pipeline
Experience with Databricks and AWS (EMR Serverless, S3)
Accommodations
If you need an accommodation to participate in the application or interview process please reach out to [email protected] or your assigned recruiter.
A Final Note:
You do not need to match every listed expectation to apply for this position. Here at Zoox, we know that diverse perspectives foster the innovation we need to be successful, and we are committed to building a team that encompasses a variety of backgrounds, experiences, and skills.
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.
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