Applied Intuition

Applied Intuition

Technical Lead Manager - Perception, Self-Driving Systems

Sunnyvale, California, United States · Contract

Sponsorship not specified$232k-$298kDetected 71 days ago
PythonC++Machine LearningDeep LearningNLPA/B Testing

About the role

  • This is a single combined model: shared backbone, multi-task heads, serving every SDS program from the same codebase.
  • The same model runs on a passenger car in Los Angeles, a truck in rural Japan, and an offroad vehicle in the Philippines.
  • Different sensor configurations, different road geometries, different weather distributions, one model.

Responsibilities

  • You will lead the team that trains, evaluates, and ships this model, and you will be hands-on in the architecture and training decisions that drive its performance.
  • 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.
  • 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.
  • Recruit, develop, and technically lead a team of perception engineers. Set high technical standards and create a culture of rigorous experimentation and measurement.
  • 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.
  • Experience building production perception systems, especially camera-only or camera-first solutions.

Requirements

  • 2+ years managing or technically leading a perception team, with ability to both set direction and contribute to architecture and training decisions directly.

Nice to have

  • Familiarity with occupancy-based scene representations, sparse query-based architectures, or temporal aggregation approaches.
  • Experience reducing or removing map dependencies in perception systems.
  • 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.
  • Please reference the job posting's subtitle for where this position will be located.
  • Don't meet every single requirement?
  • If you're excited about this role but your past experience doesn't align perfectly with every qualification in the job description, we encourage you to apply anyway.
  • You may be just the right candidate for this or other roles.

Skills

  • About Applied Intuition
  • Applied Intuition, Inc. is powering the future of physical AI.
  • 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.

Compensation

  • at Applied Intuition for eligible roles includes base salary, equity, and benefits.
  • Applied Intuition pay ranges reflect the minimum and maximum intended target base salary for new hire salaries for the position.
  • For pay transparency purposes, the base salary range for this full-time position in the location listed is: $231,900 - $298,100 USD annually.

Benefits

  • 5+ years in ML/deep learning for perception or 3D scene understanding.
  • Deep hands-on experience training and deploying vision models at scale.
  • Note that benefits are subject to change and may vary based on jurisdiction of employment.

Company info

  • 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.
  • We are looking for a Technical Lead Manager to own the perception model at the core of our autonomy stack.

This listing is sourced directly from Applied Intuition's careers page and normalized into a canonical job model.