Engineering Manager - Ml, Self-driving Systems

Applied Intuition Applied Intuition · Robotics · Sunnyvale, CA · SDS Software Engineering

Engineering Manager to lead ML teams in developing and deploying production-grade autonomous driving systems. The role involves setting technical direction, managing training and iteration cycles, working with customers, owning ML pipelines, and overseeing the full model lifecycle from prototype to embedded deployment. Requires experience in deep learning, managing technical teams, building ML training pipelines, and deploying models to edge hardware.

What you'd actually do

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.

Skills

Required

  • 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 company that has shipped autonomy to production

What the JD emphasized

  • production vehicles
  • customer hardware
  • model lifecycle
  • training at scale
  • quantization
  • device-specific optimizations
  • rigorous V&V standards

Other signals

  • production vehicles
  • customer hardware
  • model lifecycle