Senior Perception Learning Engineer

Apptronik is a human-centered robotics company developing AI-powered robots to support humanity in every facet of life. Our flagship humanoid robot, Apollo, is built to collaborate thoughtfully with people, starting with critical industries such as manufacturing and logistics, with future applications in healthcare, the home, and beyond. We operate at the cutting edge of embodied AI, applying our expertise across the full robotics stack to solve some of society's most important problems. You will join a team dedicated to bringing Apollo to market at scale, tackling the complex challenges like safety, commercialization, and mass production to change the world for the better. Job Summary As a Senior Perception Learning Engineer, you will lead research and development of advanced perception systems that empower Apptronik’s humanoid robots to understand and interact with complex human environments. Your work will focus on cutting-edge research in perception, SLAM, object detection, world modeling, and multi-sensor fusion, creating the foundation for robust autonomy in real-world settings. You will design and optimize deep learning models for real-time detection, tracking, segmentation, and scene understanding while architecting scalable pipelines for training, evaluation, and deployment. You will also integrate data from multiple modalities—Cameras, LiDAR, depth sensors, and IMUs—into unified world models that support navigation, manipulation, safety and human-robot interaction. This role requires balancing research innovation with practical engineering to deliver deployable, high-performance perception stacks. You will collaborate across Reinforcement learning teams, Platform software team and systems teams, mentor junior engineers, and contribute to shaping Apptronik’s long-term perception and autonomy roadmap. Your work will directly accelerate the development of humanoid robots that can safely operate in human spaces, adapt to dynamic environments, and extend human capability. Responsibilities 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. 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 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 sof ... (truncated, view full listing at source)