Engineering Manager (Perception, Self-Driving Systems)

Exclusive opportunity

Onsite

Engineering Manager (Perception, Self-Driving Systems)

Applied Intuition

Engineering Manager (Perception, Self-Driving Systems)

Skills

Deep LearningPythonMachine Learning

1 day ago

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Important information

Contract type:

Permanent

Salary:

$267,000 – $354,000 / year

Location:

Sunnyvale, CA, USA

Work mode:

Onsite

Published on:

22 October 2026

What they need

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 a Technical Lead Manager to own the perception model at the core of our autonomy stack
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
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
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
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
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
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
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
Work directly with OEM customer programs to understand sensor configurations, target ODDs, and performance requirements. Translate these into model architecture and data strategy
Recruit, develop, and technically lead a team of perception engineers. Set high technical standards and create a culture of rigorous experimentation and measurement- 2+ years managing a perception team, with ability to both set direction and contribute to architecture and training decisions directly
5+ years in ML/deep learning for perception or 3D scene understanding. Deep hands-on experience training and deploying vision models at scale
Experience building production perception systems, especially camera-only or camera-first solutions
Strong software engineering in Python and C++, comfortable across the stack from training code to onboard inference integration
Track record deploying perception models to embedded hardware under real-time latency and compute constraints, including device-specific optimizations
Experience scaling perception models across multiple geographies, sensor setups, or vehicle platforms
Bachelors degree in any field, or equivalent combination of education and work experience
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
Experience at an AV company that has shipped perception to production
Experience with closed-loop simulation for perception model evaluation (neural sim, log sim, scenario-based testing)
Familiarity with occupancy-based scene representations, sparse query-based architectures, or temporal aggregation approaches
Deep familiarity with transformer-based architectures for 3D perception, BEV representations, multi-task learning, and dense prediction
Experience reducing or removing map dependencies in perception systems
Background in autolabel pipelines, data quality monitoring, or data flywheel design for perception

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