Senior Perception & SLAM Engineer for Humanoid Robots

Apptronik; Sunnyvale (CA); On-site; USD 190,000 - 235,000; Full time

Job summary

Apptronik is a human-centered robotics company building AI-powered robots to assist in manufacturing, logistics, and beyond. This role drives R&D of perception systems for Apollo, focusing on SLAM, VIO, world modeling, and learning-based perception to enable 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 integrating multi-modal data into unified world models

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.
• Hands-on experience with modern AI frameworks (PyTorch, JAX, TensorFlow) and CV / 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) under compute, latency, and thermal constraints.
• Track record of shipping ML/Perception or SLAM systems from R&D into production robotics platforms.

Responsibilities

• Lead the design, development, and optimization of 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, 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., IsaacSim) with real-world datasets to enhance model generalization and robustness.
• Contribute to best practices in code quality, model versioning, reproducibility, and deployment.

Skills

SLAM

VIO

World modeling

Learning-based perception

Object detection

Multi-sensor fusion

Education

MS/PhD in CS/Robotics

Tools

PyTorch

JAX

TensorFlow

OpenCV

Detectron2

YOLO

Job description

Apptronik is a human-centered robotics company building AI-powered robots to assist in manufacturing, logistics, and beyond. This role drives R&D of perception systems for Apollo, focusing on SLAM, VIO, world modeling, and learning-based perception to enable 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 integrating multi-modal data into unified world models