# Senior Software Systems Engineer

**Company:** [Zoox](https://hotfix.jobs/companies/zoox)
**Location:** Foster City, CA, San Diego, CA
**Role:** ML Engineering
**Salary:** $208k – $300k/yr
**Experience:** 5+ years
**Skills:** Python, pandas, NumPy, SQL, Data Visualization, Statistics, hypothesis testing, autonomous robotics, sensor data analysis, lidar, camera, radar, perception, prediction, planning
**Posted:** 2026-07-27

> Develop metrics and large-scale evaluation pipelines to assess fidelity of Zoox's GenAI-powered autonomous vehicle simulator. Requires 5+ years in quantitative evaluation or robotics, strong Python/data analysis skills, and statistics knowledge.

## Job Description

## Responsibilities
- Design and implement metrics to measure the accuracy of simulated sensors, agents, and virtual environments.
- Develop robust strategies for simulator evaluation, including dataset mining, and implement these in large-scale evaluation pipelines.
- Write design proposals and drive execution while proactively communicating risks to partner teams.
- Drive consensus on validation methodologies with cross-functional teams in Autonomy, Safety, and Simulation.
- Collaborate with autonomy, safety, and simulation teams to define quantitative validation criteria for simulator fidelity.
- Use exploratory data analysis to root-cause gaps in simulator performance.

## Requirements
- 5+ years of experience in quantitative evaluation, autonomous robotics, or sensor data analysis (lidar, camera, radar).
- Proficiency in Python and data analysis (e.g., Pandas, NumPy, SQL).
- Strong foundation in data visualization.
- Working knowledge of applied statistical methods (e.g., histograms, probability distributions, simple hypothesis testing).
- Strong communication skills and thrives in dynamic environments by proactively seeking clarity.
- BS, MS, or Ph.D. in Computer Science, Robotics, or a related field.

## Nice-to-Haves
- Experience with autonomous vehicle software stack (e.g., Perception, Prediction, Planning).
- Experience implementing large-scale evaluation pipelines.
- Experience using applied statistics for system validation (e.g., confidence intervals, p-values).

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