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Salary: USD 220,000 - 250,000 / annual
Apptronik is a human-centered robotics company developing AI-powered humanoid robots (Apollo) to support humanity across manufacturing, logistics, healthcare, and beyond. The company operates at the cutting edge of applied AI, tackling the full robotics stack from safety to commercialization and mass production.
In this role, you will drive research and development of advanced perception systems that enable Apptronik's humanoid robots to understand and interact with complex human environments. Your work will focus on SLAM (Simultaneous Localization and Mapping), visual-inertial odometry, world modeling, learning-based perception, object detection, and multi-sensor fusion to create the foundation for robust autonomy in real-world settings.
Key responsibilities include:
- 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 integrating 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 and 3D scene understanding 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 for real-time compatibility with locomotion and manipulation pipelines.
- Guide integration of synthetic data (e.g., IsaacSim) with real-world datasets to enhance model generalization and robustness.
- Contribute to best practices in code quality, model versioning, reproducibility, and deployment.
You will work across Reinforcement Learning teams, Platform Software teams, and Systems teams, directly shaping Apptronik's perception and autonomy roadmap.
REQUIREMENTS:
- 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 (visual-inertial odometry), 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 (OpenCV, Detectron2, YOLO, 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 (ORB-SLAM, VINS, Kimera, Cartographer) or learning-based SLAM systems.
- Strong classical computer vision skills complementing learning-based approaches.
- Expertise in model acceleration, quantization, or compression (TensorRT, ONNX Runtime).
- Familiarity with real-time frameworks and middleware (ROS 2, GStreamer, zero-copy pipelines).
- Knowledge of synthetic data generation and domain adaptation techniques.
- Contributions to open-source robotics or vision software stacks.
