You will drive 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 heavily on SLAM, visual-inertial odometry, world modeling, and learning-based perception, alongside object detection 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, scene understanding, and state estimation while contributing to 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 SLAM and perception stacks. You will collaborate across Reinforcement Learning teams, Platform Software team, and Systems teams, and contribute to shaping Apptronik’s 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.

Core Responsibilities

• Lead Design, develop, and optimize perception and SLAM pipelines for humanoid robots, including visual-inertial odometry, mapping, localization, 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 state estimation and mapping in dynamic human-centered environments.
• Contribute to scalable data pipelines, training infrastructure, and inference frameworks to accelerate model development, evaluation, and deployment.
• Drive research and deployment of learning-based models for SLAM, 3D scene understanding, and perception optimized for humanoid locomotion, manipulation, and human-robot interaction.
• Implement performance profiling, regression testing, and telemetry systems to ensure perception and SLAM 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.
• Contribute to best practices in code quality, model versioning, reproducibility, and deployment.

Required Qualifications

• MS/PhD in Computer Science, Robotics, Computer Engineering, or related field.
• 3- 5+ years building and shipping perception and/or SLAM systems in robotics or real-time vision applications.
• Strong background in SLAM, VIO, probabilistic state estimation, and deep learning for computer vision.
• Practical expertise in detection, segmentation, multi-object tracking, 3D perception, and learning-based mapping/localization.
• 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, factor graphs, SLAM, VIO).
• Experience deploying optimized perception or SLAM models on edge hardware (GPU/NPU/embedded platforms) under compute, latency, and thermal constraints.
• Track record of shipping ML/Perception or SLAM systems from R&D into production robotics platforms.

Preferred Experience

• Experience with humanoid robots, bipedal locomotion, and manipulation tasks.
• Experience with modern SLAM frameworks (e.g., ORB-SLAM, VINS, Kimera, Cartographer) or learning-based SLAM systems.
• Strong classical computer vision skills (geometry-based methods, feature extraction) complementing learning-based approaches.
• Expertise in model acceleration, quantization, or compression (TensorRT, ONNX Runtime).
• Familiarity with real-time frameworks and middleware such as ROS 2, GStreamer, or zero-copy pipelines.
• Knowledge of synthetic data generation and domain adaptation techniques for training perception and SLAM models.
• Contributions to open-source robotics or vision software stacks.

The annual salary range is $220,000 - $250,000