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Applied Intuition is a $15B-valued AI infrastructure company (founded 2017) powering physical AI across automotive, defense, trucking, construction, mining, and agriculture. The company serves 18 of the top 20 global automakers and the US military, with offices across North America, Europe, and Asia.
You will lead the Autonomy Storage Infrastructure team within the Data & Compute organization. This team is responsible for the complete data lifecycle—collection, ingestion, storage, querying, and retrieval—at hundreds of petabytes scale. You'll partner with all business units to define the technical roadmap for storage infrastructure, ensuring reliable support for both external products and internal tooling across cloud, hybrid, and on-premises deployments.
Key responsibilities:
- Lead, grow, and mentor a team of data infrastructure engineers, fostering technical excellence, ownership, and collaboration
- Define and drive the technical roadmap for storage infrastructure, balancing short-term delivery with long-term scalability
- Own the architecture and reliability of systems handling the full data lifecycle across all deployment environments (cloud, hybrid, on-prem) and regions
- Drive engineering decisions around scalable data ingestion, indexing, storage, and retrieval for high-volume autonomous vehicle and physical AI workloads
- Establish and uphold engineering best practices, code quality standards, and operational excellence
- Recruit top engineering talent and build an inclusive, high-performing team
This is a highly cross-functional leadership role requiring deep technical credibility, strong people management skills, and a bias for ownership. Engineering managers at Applied Intuition are expected to be hands-on leaders who actively engage in technical direction, not just people management.
The company operates primarily in-office (5 days/week from the Sunnyvale headquarters), though occasional flexibility is available for morning remote work or schedule adjustments for personal commitments.
REQUIREMENTS:
- Bachelor's degree in Computer Science, Software Engineering, or equivalent
- 6+ years of professional software engineering experience, with 2+ years in an engineering management or tech lead role
- Hands-on experience with large-scale open-source data technologies (Spark, Hudi/Iceberg, LanceDB, dbt, etc.)
- Strong track record of leading teams that build and operate high-scale distributed data systems
- Experience managing engineers across the full data lifecycle in a production environment
- Deep understanding of containerization, cloud-native architectures, and modern software development workflows
- Strong cross-functional communication skills and ability to influence technical direction at a senior level
NICE TO HAVE:
- Multi-cloud (GCP, AWS, Azure) or multi-region data platforms, including data-residency requirements
- Streaming and change data capture technologies (Kafka, Pub/Sub, Flink, Debezium) at high scale
- Familiarity with data quality, data profiling, and data governance frameworks
- Building and operating observability or monitoring for data systems at scale
- Experience in high-growth environments where data infrastructure evolved rapidly
- Prior exposure to automotive, defense, robotics, or physical AI domains
