Engineering Manager - ML, Self-Driving Systems

About Applied Intuition 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 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. About the role Applied Intuition builds the software infrastructure for autonomous vehicles across passenger cars, trucking, mining, and defense. Our Self-Driving Systems (SDS) team develops production-grade autonomy stacks deployed on real vehicles across multiple continents, from highway trucking in Japan to urban ADAS in the United States and Europe. We are looking for an Engineering Manager to lead ML teams within SDS Core. This is a large organization spanning perception model development, agent prediction, E2E driving models, ML engineering infrastructure, and the offboard training pipelines that power them. Your teams will train models, iterate on architecture and data, run simulation and on-road experiments, and ship into production vehicles on timelines measured in months. The same model architecture must serve L2 ADAS, L4 trucking, and mining from a common codebase, while meeting the distinct safety and performance requirements of each. At Applied Intuition, you will: Set the technical direction across multiple ML workstreams: the foundation model, shared backbone, and task heads that enable end-to-end driving, plus agent prediction and model optimization. The core challenge is commonization across verticals so one model serves ADAS, trucking, and mining without per-vertical forks. Lead rapid training and iteration cycles across your teams. Models ship into production vehicles on quarterly release cycles with direct impact on customer programs. You will be close enough to the data and results to know when something is off. Work directly with OEM customers and program teams to translate vehicle platform constraints into model architecture and delivery plans. You are accountable for models running on customer hardware, not benchmarks on a leaderboard. Own the offboard ML pipelines that determine iteration speed: training infrastructure, data curation, autolabel quality, and the evaluation systems that connect offboard metrics to on-vehicle driving outcomes. Manage the full model lifecycle from prototype to embedded deployment, including training at scale, quantization, and device-specific optimizations. Models must meet rigorous V&V standards for vehicles on public roads. Recruit, develop, and retain strong engineers in a competitive market. You will shape the team's structure, culture, and technical standards as it continues to grow. We’re looking for someone who has: 5+ years in deep learning. Hands-on experience guiding teams in state-of-the-art ML development and deployment. 4+ years managing deeply technical product development teams Experience building ML training pipelines at scale: data management, distributed training, experiment tracking, model evaluation. Track record deploying ML models to embedded or edge hardware, including quantization, pruning, and device-specific optimizations. Strong software engineering in Python and C++, comfortable across the full stack from training code to onboard inference. Experience managing through architecture transitions (classical to learned, modular to end-to-end) while maintaining production reliability. Nice to have: Familiarity with occupancy-based scene representations, dense prediction heads, or sparse query-based architectures. Experience with closed-loop simulation for ML model evaluation (neural sim, log sim, scenario-based testing). Background in data flywheel design: automated ingestion, curation, quality monitoring, and dataset refresh workflows. Multi-domain ML development: training one model architecture across different sensor configs, vehicle types, or geographies. Experience at an AV co