Why This Role Stands Out
This hybrid role offers an exciting opportunity to lead the architecture and development of cutting-edge robotics and edge AI systems, bridging physical automation with advanced 5G infrastructure. You'll thrive here if you possess a passion for middleware engineering, embedded systems, and optimizing complex workflows in simulation and real-world deployments. Apply now to shape the future of intelligent automation!
Quick Overview
Job Description
Senior/Lead Robotics and Edge Integration Engineer
- Location: Toronto, Ontario , Canada - Remote
- Long term Contract
- Department: Robotics & Edge AI Engineering
Job Purpose
As a Senior/Lead Robotics and Edge Integration Engineer , you will lead the architecture, development, and deployment of intelligent edge-computing systems and robotic platforms. You will bridge the gap between autonomous physical systems, industrial automation protocols, and 5G-enabled edge infrastructure. This role focuses on building resilient middleware ecosystems, optimizing embedded edge compute modules, and validating complex robotics workflows in high-fidelity simulations before deploying them to real-world industrial environments.
Key Responsibilities
- Middleware & Communication Engineering: Design, optimize, and maintain low-latency communication fabrics utilizing ROS 2, DDS (Data Distribution Service), and MAVLink for dependable multi-agent and embedded robotic systems.
- Industrial Protocol Integration: Implement and standardize machine-to-machine interfaces such as OPC UA and fleet management protocols like VDA 5050 to ensure seamless interoperability between autonomous mobile robots (AMRs) and automated factory systems.
- Embedded Edge Systems & Hardware Acceleration: Architect and optimize software stacks on embedded Linux environments and edge hardware platforms, specifically NVIDIA Jetson systems, leveraging hardware-accelerated processing for real-time applications.
- Simulation-Driven Development: Build, configure, and maintain advanced digital twins and robotic simulations using NVIDIA Isaac Sim and Isaac Lab to test autonomy algorithms, edge workloads, and fleet coordination protocols safely in virtual environments.
- Behavior Optimization & Controls: Design, implement, and debug complex robotic behaviors and state machines utilizing tools like Groot and BehaviorTree.CPP.
- Technical Leadership & Collaboration: Lead and mentor junior/mid-level robotics engineers, collaborate with cloud-native infrastructure teams to deploy edge gateways, and champion rigorous software engineering best practices within the robotics domain.
Required Qualifications
- Experience: 6+ years of experience in software development for autonomous systems, robotics, or edge computing, with a proven track record of shipping production-grade embedded or physical robotic software.
- Robotics Middleware: Deep expertise in ROS 2 (Robot Operating System) architecture, node lifecycle management, lifecycle nodes, custom messages, and DDS tuning (e.g., Fast DDS, Cyclone DDS).
- Industrial Automation & Fleets: Demonstrated hands-on experience integrating systems with industrial protocols like OPC UA or deploying VDA 5050 standards for AGV/AMR fleet coordination.
- Embedded Linux & Edge Compute: Extensive experience configuring and optimizing custom embedded Linux builds and implementing software pipelines on edge compute hardware, with deep technical knowledge of the NVIDIA Jetson platform ecosystem (JetPack, DeepStream, TensorRT).
- Simulation Platforms: Proficient in building physical simulations, sensor models, and synthetic data generation environments within NVIDIA Isaac Sim or Isaac Lab.
- Behavior Orchestration: Familiarity with designing and debugging hierarchical state machines and behavior trees, specifically using Groot.
- Core Software Skills: Strong proficiency in modern C++ (14/17/20) and Python, alongside structured debugging skills for real-time embedded environments.
Preferred Qualifications
- Experience designing and utilizing MAVLink-based communication systems for unmanned aerial vehicles (UAVs) or autonomous systems.
- Background in telecommunications or networking, particularly deploying robotics and edge workloads over private 5G or localized LTE networks.
- Academic degree (BS, MS, or PhD) in Robotics, Computer Science, Electrical Engineering, Mechanical Engineering, or a closely related quantitative field.
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