Senior Perception Learning Engineer
$190,000–$235,000 year
On-site · Mountain View Santa Clara County, California, United States
Job Summary
Senior Perception Learning Engineer to lead the design, development, and optimization of perception pipelines for humanoid robots, including multi-sensor fusion, real-time perception on edge devices, and integration with locomotion/manipulation systems. Responsibilities include building detection/tracking/segmentation/pose estimation; developing training/inference pipelines; collaborating with planning/control/hardware teams; guiding synthetic data usage (e.g., IsaacSim) for robustness; mentoring junior engineers and promoting code quality, model versioning, and deployment best practices. Requires MS/PhD in relevant field and 3-5+ years of hands-on robotics perception experience, plus strong Deep Learning and CV toolchain knowledge; experience shipping perception systems to production. Salary range: $190,000-$235,000 per year.
Required Qualifications
- MS/PhD in Computer Science, Robotics, Computer Engineering, or related field
- 3-5+ years of experience building and deploying perception systems for robotics, autonomous vehicles, or real-time vision applications
- Strong background in deep learning for computer vision, with practical expertise in detection, segmentation, multi-object tracking, and 3D perception
- Hands-on experience with PyTorch, JAX, TensorFlow; OpenCV, Detectron2, YOLO; foundation models for perception and language (e.g., SAM, CLIP, DINOv2, Flamingo)
- Proficiency in Python and modern C++, with software engineering fundamentals (version control, testing, CI/CD)
- Deep understanding of 3D geometry, camera models, and probabilistic estimation (EKF/UKF, SLAM, VIO)
- Experience deploying optimized models on edge hardware (GPU/NPU/embedded platforms) under compute/latency/thermal constraints
- Track record of shipping ML/Perception systems from R&D into production robotics platforms
- Preferred: humanoid robots, bipedal locomotion and manipulation; model acceleration/quantization (TensorRT, ONNX Runtime); ROS 2, GStreamer; synthetic data/domain adaptation; open-source contributions
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