Principal AI/ML Robotics Engineer - Environmental Perception
in
80331, München, Bayern, Deutschland
Verfasst am 2026-10-14
Unternehmen:
Analog Devices, Inc.
Vollzeit
position Verfasst am 2026-10-14
Berufliche Spezialisierung:
-
Software Entwicklung
Robotik, Künstliche Intelligenz Ingenieur, Maschinelles Lernen
Stellenbeschreibung
** Principal Engineer – AI for Visual Perception, SLAM, and Sensor Fusion
***
* Location:
** Munich
** Team:
** Edge AI Group, Analog Devices Inc. (ADI)
Analog Devices’ Edge AI team is building the next generation of intelligent sensing systems that reshape how machines perceive and interact with the physical world. From deeply embedded algorithms to scalable AI applications, we work across the full stack to fuse advanced hardware with real-time intelligence. Our mission: bring AI to the Edge — where decisions meet the real world.
We are looking for a
** Principal Engineer
** with deep domain expertise in
** visual odometry, sensor fusion, SLAM**, and
** AI-based perception
** to lead the development of real-world localization and mapping solutions. You will work at the intersection of embedded sensing, robotics, and machine learning, helping create systems that can understand, navigate, and adapt to their environments autonomously — in factories, warehouses, vehicles, and beyond.
You will join a high-impact team developing foundational components for ADI’s next wave of perception-enabled solutions. This is a hands-on leadership role for someone who thrives on solving open-ended challenges, defining technical roadmaps, and mentoring other engineers.
** Responsibilities
* ** Lead the design and deployment of
** SLAM, VIO, and multi-sensor fusion systems
** that enable robust, real-time mapping and localization across diverse environments.
* Drive
** algorithm development and evaluation
** for perception tasks, including
** pose estimation, depth reconstruction, loop closure, map optimization**, and semantic understanding.
* Architect AI pipelines that combine
** classical and learned methods** (e.g., CNNs, transformers, foundation models) for environmental understanding at the edge.
* Develop real-world and simulation-based datasets for benchmarking and validation; guide sensor selection and system integration.
* Collaborate cross-functionally with embedded, hardware, and systems engineers to bring scalable solutions from research to production.
* Stay current with the state of the art in robotics, SLAM, Edge AI, and self-supervised learning — and guide the team in adopting innovative technologies.
* Contribute to the broader AI platform architecture, CI/CD pipelines, and MLOps practices.
** Qualifications**
* ** 10+ years
** of experience in robotics, computer vision, or AI, including
** 5+ years in SLAM, VO, or sensor fusion**; with
** 3+ years in a technical leadership role**.
* M.S. or Ph.D. in Robotics, Computer Science, Electrical Engineering, or related field.
* Demonstrated expertise in at least one of the following: +
** Visual-inertial odometry (VIO)** +
** Multi-sensor fusion (camera, LiDAR, IMU, encoders)** +
** 3D SLAM and mapping in dynamic environments** +
** AI-based perception models for real-time localization
*** Proven track record of deploying perception algorithms in real-world systems (e.g., autonomous robots, drones, AR/VR, self-driving platforms).
* Strong programming skills in
** Python and C++**, with experience in frameworks like
** PyTorch**,
** ROS**,
** g2o**, or
** Ceres**.
* Comfortable working with
** real-time systems**, embedded platforms, or simulators such as Gazebo, Isaac Sim, or Unreal Engine.
* Familiar with
** MLOps
* * and software best practices: CI/CD, containerization (Docker), orchestration (Kubernetes), etc.
* Excellent communicator and collaborator — capable of leading multi-disciplinary teams and influencing stakeholders across hardware, software, and business domains.
** Bonus Experience (Nice to Have)
*** Experience with
** self-supervised learning**,
** foundation models for robotics**, or
** transformer-based perception architectures**.
* Familiarity with…
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