Senior Perception Learning Engineer

ESSENTIAL DUTIES AND RESPONSIBILITIES or KEY ACCOUNTABILITIES

Lead the design, development, and optimization of perception pipelines for humanoid robots, including object detection, tracking, segmentation, pose estimation, and scene understanding.

Develop multi-sensor fusion frameworks that integrate cameras, LiDAR, depth sensors, and IMUs for robust real-time perception in dynamic human-centered environments.

Architect and maintain scalable data pipelines, training infrastructure, and inference frameworks to accelerate model development, evaluation, and deployment.

Drive research and deployment of deep learning models optimized for humanoid locomotion, manipulation, and human-robot interaction.

Implement performance profiling, regression testing, and telemetry systems to ensure perception modules meet strict latency, accuracy, and reliability requirements on edge devices.

Collaborate with planning, control, and hardware teams to define perception-to-action interfaces, ensuring real-time compatibility with locomotion and manipulation pipelines.

Guide the integration of synthetic data (e.g., simulation frameworks like IsaacSim) with real-world datasets to enhance model generalization and robustness.

Mentor junior engineers and contribute to best practices in code quality, model versioning, reproducibility, and deployment.

EDUCATION and/or EXPERIENCE

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 modern AI frameworks (PyTorch, JAX, TensorFlow) and computer vision / multi-modal libraries such as OpenCV, Detectron2, YOLO, and foundation models for perception and language (e.g., SAM, CLIP, DINOv2, Flamingo)

Proficiency in Python and modern C++, with strong 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, and thermal constraints.

Track record of shipping ML/Perception systems from R&D into production robotics platforms.