Avride logo
Avride
Verified live 2d ago

Autonomous Vehicle Metrics and Evaluation Data Scientist - Analytics

Brief overview

Austin, TXIn-person
2+ yrsMinimum
19 H-1B approvalsDept. of Labor
1 green cardsCertified filings
PythonSQLStatistical analysisA/B testingConfidence intervalsRegressionMetric designData pipeline developmentMachine learningBig data toolsAutonomous systems

About the company

Avride logo
Avrideavride.ai

Avride is a developer and operator of autonomous vehicles and delivery robots.

Visa sponsorship history

2 years sponsoring, last filed FY2026

Data powered by U.S. Department of Labor. This does not guarantee sponsorship for this specific role.
19H-1B approved
100%approval rate
17new H-1B hires
1PERM certified
$150,000median wage / yr
H-1B Petition ApprovalsVisas USCIS actually granted: the strongest sign the company sponsors.
202311
20246
20251
20261
LCA Certified ApplicationsAn early filing step, not a visa approval: it signals intent, not confirmed sponsorship.
20238
20252
20266
Green Card (PERM) FilingsCertified green card filings: a long-term commitment to international hires.
20261
Top sponsored roles
Software EngineerTechnical Project ManagerMachine Learning EngineerAdministrative Service ManagerChief Executive Officer

Job description

About the Team

Our team is dedicated to advancing the future of autonomous driving technology by developing the metrics and evaluation frameworks that ensure the safety, performance, and readiness of self-driving systems. We work closely with engineering teams to analyze real-world and simulated driving data, enabling data-driven decisions that accelerate the commercialization of autonomous vehicles. Our work directly impacts critical questions such as: ‘How well does the vehicle handle complex intersections? Does it provide a passenger-safe experience during maneuvers? How do we statistically validate readiness for urban deployment?’

About the Role

We are seeking a skilled Data Scientist to join our team and play a pivotal role in defining and evaluating the performance of autonomous vehicle systems. You will design and implement quantitative metrics to assess driving behavior, safety compliance, and passenger comfort. Your work will involve analyzing large datasets, applying statistical methods, and collaborating with cross-functional teams to ensure our self-driving systems meet the highest standards of safety and reliability. This role offers the opportunity to make a significant impact on the future of autonomous driving technology.

What You'll Do

  • Analyze data from real-world and simulated autonomous vehicle experiments to evaluate system performance.
  • Develop and refine quantitative metrics (both rule-based and ML-driven) to assess driving behavior, safety, and passenger comfort.
  • Apply statistical inference and hypothesis testing to validate the sensitivity and reliability of metrics.
  • Collaborate with autonomy software, simulation, and safety teams to prioritize improvements based on data-driven insights.
  • Build scalable data pipelines to automate metric computation and reporting.
  • Review driving logs and participate in test rides to ground analyses in real-world performance.

What You'll Need

  • 2+ years of experience in data science, applied statistics, or metrics development.
  • Proficiency in Python and SQL for data analysis.
  • Strong background in statistical methods (e.g., A/B testing, confidence intervals, regression).
  • Experience designing metrics to evaluate complex systems (e.g., robotics, autonomous systems, or large-scale software).
  • Ability to communicate technical findings to cross-functional stakeholders.

Nice to Have

  • Experience with machine learning (e.g., model evaluation, feature engineering).
  • Familiarity with big data tools (Spark, Hadoop, MapReduce).
  • Knowledge of traffic regulations or automotive safety standards (e.g., NHTSA).

Candidates are required to be authorized to work in the U.S. The employer is not offering relocation sponsorship, and remote work options are not available.

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