Data Quality Specialist

Applied Intuition, Inc. is powering the future of physical AI. Founded in 2017 and now valued at $15 billion, the Silicon Valley company is creating the digital infrastructure needed to bring intelligence to every moving machine on the planet. Applied Intuition services the automotive, defense, trucking, construction, mining and agriculture industries in three core areas: tools and infrastructure, operating systems, and autonomy. Eighteen of the top 20 global automakers, as well as the United States military and its allies, trust the company’s solutions to deliver physical intelligence. Applied Intuition is headquartered in Sunnyvale, California, with offices in Washington, D.C.; San Diego; Ft. Walton Beach, Florida; Ann Arbor, Michigan; London; Stuttgart; Munich; Stockholm; Bangalore; Seoul; and Tokyo. Learn more at applied.co.

We are an in-office company, and our expectation is that full-time employees primarily work from their Applied Intuition office 5 days a week. However, we also recognize the importance of flexibility and trust our employees to manage their schedules responsibly. This may include occasional remote work, starting the day with morning meetings from home before heading to the office, or leaving earlier when needed to accommodate family commitments. This in-office expectation does not apply to contractor positions

About the role

As a Data Quality Specialist you will be a critical component of our autonomous vehicle data pipeline. You will ensure the integrity and quality of data collected from our test vehicles, which is essential for training and improving our self-driving systems. Your meticulous review and verification processes will help maintain high data standards and facilitate the development of more accurate and reliable autonomous driving algorithms.

At Applied Intuition, you will:

• 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

We're looking for someone who has: