Senior Perception Engineer

John Deere John Deere · Industrial · Santa Clara, CA +1 · Product Engineering (CA)

Senior Perception Engineer role focused on developing and deploying applied ML models for autonomous systems, with responsibilities spanning the full autonomy stack from data ingestion to model deployment on embedded platforms. The role requires strong C++ and ML skills, with an emphasis on production reliability and system integration.

What you'd actually do

  1. Develop and maintain perception capabilities across the full autonomy stack — working from onboard C++ systems and sensor interfaces up through applied ML models that interpret the environment.
  2. Develop and maintain key components of the perception pipeline, including data ingestion, labeling, model execution, and downstream interfaces used by planning and controls.
  3. Diagnose and resolve perception failures using logs and sensor data — reconstructing system behavior when real‑time debugging isn’t possible and driving issues to root cause.
  4. Implement and optimize C++ modules running on embedded and edge compute platforms, ensuring real‑time performance, robustness, and clean integration with the broader autonomy system.
  5. Train, evaluate, and deploy applied ML models for detection, segmentation, tracking, and scene understanding — focusing on production reliability rather than research novelty.

Skills

Required

  • modern C++
  • applied machine learning for perception
  • training ML models
  • evaluating ML models
  • deploying ML models
  • PyTorch
  • TensorFlow
  • debugging skills
  • log-driven environments
  • sensor data (camera, LiDAR, radar)
  • perception pipelines
  • onboard compute
  • middleware
  • cloud-based data workflows
  • high-quality, maintainable code
  • multidisciplinary engineering team collaboration

Nice to have

  • ROS 2
  • deploying ML models to embedded platforms
  • optimizing for latency, memory, or power constraints
  • classical computer vision
  • sensor fusion
  • tracking algorithms
  • static analysis
  • code quality tools
  • simulation environments
  • synthetic data generation
  • system-level testing frameworks
  • agricultural, mining, construction, or other off-highway autonomous systems
  • Linux development
  • build systems (CMake, Bazel, or colcon)
  • containerized workflows (Docker)
  • concurrency
  • performance constraints
  • real-time considerations in robotics or embedded systems

What the JD emphasized

  • production reliability rather than research novelty
  • substantial work in modern C++
  • applied machine learning for perception
  • Strong debugging skills
  • Experience working with sensor data
  • Ability to work effectively across system boundaries
  • Demonstrated ability to deliver high-quality, maintainable code

Other signals

  • Develop and maintain perception capabilities across the full autonomy stack
  • Train, evaluate, and deploy applied ML models for detection, segmentation, tracking, and scene understanding
  • Implement and optimize C++ modules running on embedded and edge compute platforms
  • Collaborate closely with robotics, systems, and platform teams