Engineering Manager - Autonomy Data - applied

Polluxa, Inc.; Sunnyvale (CA); Hybrid; USD 180,000 - 260,000; Full time

Job summary

Applied Intuition, Inc. in Silicon Valley seeks an Engineering Manager for the Autonomy Data team to lead a world-class group focused on data quality, labeling workflows, and data mining. You will drive the adoption of foundation models to scale labeling, quality assurance, and data discovery.

This role partners with ML teams to accelerate autonomy development, design scalable data engines, and reduce annotation costs while advancing model iteration cycles.

Qualifications

• Lead a world-class engineering team focused on data-driven autonomy development.
• Design and optimize data quality systems, labeling workflows, and data mining infrastructure.
• Integrate foundation models (LLMs, VLMs, multimodal models) to automate labeling and QA.

Responsibilities

• Grow and manage a team of world-class engineers with the goal of delivering high-quality, well-labeled data and identifying critical edge cases for autonomy
• Prioritize development across data quality systems, intelligent labeling workflows, and large-scale data mining infrastructure
• Lead the integration of foundation models (LLMs, VLMs, and multimodal models) to automate and enhance labeling, quality assurance, and data discovery
• Evolve our data engine architecture to scale high-fidelity labels, reduce annotation costs, and accelerate ML iteration cycles

Job description

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 http://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 an Engineering Manager on the Autonomy Data team, you'll lead a world-class group of engineers focused on revolutionizing how we produce, curate, and leverage high-quality data to accelerate autonomy development. Your team will work across three key areas: Data Quality, where we build systems to measure, validate, and improve the integrity of our datasets; Labeling Workflows, where we design intelligent annotation pipelines that combine human expertise with AI-powered automation; and Data Mining, where we surface rare, high-value driving scenarios from massive multi-modal fleet data.

You'll drive the adoption of foundation models and cutting-edge AI techniques to scale these capabilities, setting technical direction and team goals that align with our model development, safety, and deployment milestones.

AT APPLIED INTUITION, YOU WILL:

• Grow and manage a team of world-class engineers with the goal of delivering high-quality, well-labeled data and identifying critical edge cases for autonomy
• Prioritize development across data quality systems, intelligent labeling workflows, and large-scale data mining infrastructure
• Lead the integration of foundation models (LLMs, VLMs, and multimodal models) to automate and enhance labeling, quality assurance, and data discovery
• Evolve our data engine architecture to scale high-fidelity labels, reduce annotation costs, and accelerate ML iteration cycles