Autonomous Vehicle Data Quality Analyst

Decisive Point; Sunnyvale (CA); On-site; USD 90,000 - 130,000; Full time

Job summary

Applied Intuition is seeking a Data Quality Specialist to ensure the integrity of autonomous vehicle data from test drives. You will develop automation for data verification, identify data anomalies, and support labeling for model training.

Collaboration with engineering teams helps refine data collection methods and quality standards. Ideal candidates have 2+ years in data analysis or QA, familiarity with annotation tools, and experience with LiDAR/radar/camera data.

Qualifications

• 2+ years experience in data analysis, quality assurance, or related technical field.
• Familiarity with data annotation tools and workflows.
• Experience with data validation techniques and quality control processes for AV domain.
• Attention to detail and analytical mindset.
• Experience with sensor data (LiDAR, radar, camera) from autonomous vehicles.
• Strong problem-solving abilities and communication skills.

Responsibilities

• Review and validate data collected from test drives to ensure completeness, accuracy, and relevance.
• Develop and implement automation scripts to streamline data verification processes.
• Identify anomalies, inconsistencies, and quality issues in collected datasets.
• Assist with data annotation and labeling to support machine learning model training.
• Document quality standards and create comprehensive reports on data quality metrics.
• Collaborate with engineering teams to improve data collection methodologies.
• Support the continuous improvement of data verification workflows.

Skills

Data analysis

Quality assurance

Data validation

Attention to detail

Sensor data handling

Communication

Tools

Python

Linux

Jira

Confluence

Google Workspace

Job description

Applied Intuition is seeking a Data Quality Specialist to ensure the integrity of autonomous vehicle data from test drives. You will develop automation for data verification, identify data anomalies, and support labeling for model training.

Collaboration with engineering teams helps refine data collection methods and quality standards. Ideal candidates have 2+ years in data analysis or QA, familiarity with annotation tools, and experience with LiDAR/radar/camera data.