Data Quality Analyst- Autonomous Vehicles

🏢 Avride · 📍 Austin, Texas

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Position Summary & Responsibilities

About the Role

Our autonomous vehicles encounter a wide variety of real-world situations on city streets. These situations need to be identified, categorized, and described in a structured way so they can be analyzed and used to evaluate the quality of our technology.

We are looking for a Data Quality Analyst who will help us validate and analyze real-world data related to the situations our autonomous vehicles encounter every day. In this role, you will work closely with production, develop quality metrics, and ensure consistent dataset quality across multiple annotation workflows.

You will be involved in the analysis and validation cycle: understanding test cases, working with collected data, validating its quality and completeness, analyzing results, identifying behavioral issues and coverage gaps, and determining where additional testing or investigation may be needed.

This is a data-focused role. You will work primarily with already collected data, test results, and driving scenarios, using analytical tools to understand vehicle behavior and support the testing and engineering teams.

This position is ideal for someone who is detail-oriented, comfortable working with complex datasets, and motivated to enhance data quality and annotation efficiency through analytical and technical approaches.

What You'll Do

Data Analytics

Analyze collected data across different road scenarios;
Analyze individual samples of data in detail to understand what happened and which factors may have affected situation;
Identify edge cases and scenarios that require additional investigation;
Monitor quality metrics on a regular basis and investigate anomalies;
Analyze dataset balance and completeness across locations, scenarios, and labels;
Assess how new scenes contribute not just more data, but better data.

Operational Routine & Controls

Regularly reviewing annotation batches (including repetitive checks) to make sure they meet quality and coverage expectations;
Maintain simple control dashboards and reports, update them on a weekly basis, and follow up on issues;
Take care of many small but important operational tasks that keep the annotation process stable and predictable.

Support & Automation

Use Pyt

🎯 Candidate Preparation Guide For Avride Applicants

Key Role Competencies & Evaluation Highlights

Key Role Focus:

Focuses on safety-critical robotics engineering, edge-case autonomous driving resilience, and rigorous simulation-to-reality validation.

📋 Preparation Priorities (Key Areas to Clarify):

1. Hand-waving safety edge cases or underestimating real-world deployment hazards.
2. Over-reliance on idealized simulation environments without real-world hardware validation.
3. Poor cross-functional collaboration between perception, planning, and infrastructure teams.

✨ Recommended Role Alignment Hook:

I build bulletproof perception and motion planning pipelines that safely navigate autonomous vehicles through chaotic urban realities.

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