Technical Lead Manager - Perception, Self-driving Systems

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

Technical Lead Manager for Perception, Self-Driving Systems, responsible for the end-to-end ownership of a core perception model that generalizes across various vehicles, sensors, and environments. The role involves leading a team to train, evaluate, and deploy this production-grade model, with a focus on camera-first strategies and on-vehicle performance, including optimizations for embedded hardware.

What you'd actually do

  1. Own the perception model end-to-end: architecture, training, evaluation, and deployment. The core challenge is building a model that generalizes across geographies, road types, sensor setups, and environmental conditions without per-vertical forks.
  2. Drive a camera-first perception strategy. The goal is to progressively reduce dependencies on HD maps and lidar. How to get there is part of the job.
  3. Lead training and iteration cycles hands-on. You will be in the data, the eval dashboards, and the failure analysis. When perception regresses in a new geography or road type, you own understanding why and fixing it.
  4. Own model performance across the full deployment surface: highway, urban, residential, ramps, complex intersections, poor weather, hilly terrain. You care about on-vehicle driving outcomes, not just offline metrics.
  5. Manage the model lifecycle from training through quantization and deployment on embedded compute, including device-specific optimizations. Close the gap between what the model does offboard and what it does on the vehicle.

Skills

Required

  • 5+ years in ML/deep learning for perception or 3D scene understanding
  • Deep hands-on experience training and deploying vision models at scale
  • 2+ years managing or technically leading a perception team
  • Experience building production perception systems, especially camera-only or camera-first solutions
  • Track record deploying perception models to embedded hardware under real-time latency and compute constraints, including device-specific optimizations
  • Strong software engineering in Python and C++
  • Experience scaling perception models across multiple geographies, sensor setups, or vehicle platforms

Nice to have

  • Deep familiarity with transformer-based architectures for 3D perception, BEV representations, multi-task learning, and dense prediction
  • Familiarity with occupancy-based scene representations, sparse query-based architectures, or temporal aggregation approaches
  • Experience reducing or removing map dependencies in perception systems
  • Background in autolabel pipelines, data quality monitoring, or data flywheel design for perception
  • Experience with closed-loop simulation for perception model evaluation (neural sim, log sim, scenario-based testing)
  • Experience at an AV company that has shipped perception to production

What the JD emphasized

  • production-grade autonomy stacks
  • generalized perception model
  • camera-first perception strategy
  • on-vehicle driving outcomes
  • device-specific optimizations

Other signals

  • production-grade autonomy stacks
  • generalized perception model
  • camera-first perception strategy
  • on-vehicle driving outcomes
  • device-specific optimizations