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About the EY.ai Lab

The EY.ai Lab is a dedicated environment for exploring how artificial intelligence can interact with and operate within the physical world. The Lab brings together humanoid and quadruped robots, sensors, edge computing, computer vision, multimodal AI, simulation technologies, and emerging Physical AI capabilities.

The Lab provides an applied R&D environment where EY teams and clients can rapidly experiment with, prototype, evaluate, and demonstrate AI-enabled physical systems before deploying them into real-world environments.

Role Overview

We are seeking a Physical AI & Robotics Engineer to develop and integrate intelligent capabilities across the EY.ai Lab's robotics and Physical AI ecosystem.

This is a hands-on engineering role at the intersection of robotics, machine learning, computer vision, multimodal AI, agentic AI, and software engineering. The successful candidate will work directly with humanoid robots, quadrupeds, sensors, cameras, edge devices, and simulation environments while developing the AI capabilities that enable these systems to perceive, reason, interact, and act within physical environments.

The role will also focus on turning experimental capabilities into modular, reusable AI tools and services that can be integrated into broader enterprise AI and agentic platforms.

In addition to technical development, the individual will contribute to client demonstrations, innovation sessions, applied research, and the ongoing operation of the Physical AI Lab.

Key Responsibilities

Research & Innovation

• Research emerging developments in Physical AI, robotics, multimodal foundation models, VLMs, VLAs, computer vision, and agentic systems.

• Evaluate emerging robotics platforms, AI models, sensors, simulation technologies, and development frameworks.

• Identify and prototype new Physical AI use cases across industries such as manufacturing, logistics, healthcare, infrastructure, and energy.

Physical AI & Applied AI Development

• Design, develop, and evaluate control architectures for humanoid, quadruped, and other robotic platforms.

• Develop multi modal data fusion pipelines using cameras, LiDAR, audio, telemetry, and other modalities for tasks such as localization, object detection, segmentation, tracking, pose estimation, scene understanding.

• Experiment with and integrate Vision-Language Models (VLMs), Vision-Language-Action (VLA) models, multimodal foundation models, and other emerging Physical AI approaches.

• Train, fine-tune, evaluate, and deploy machine learning models for robotics and Physical AI use cases.

• Develop and test robotic capabilities using simulation environments such as NVIDIA Isaac Sim, Gazebo, MuJoCo, or equivalent technologies.

• Explore the use of LLMs and agentic AI to enable robots and physical systems to reason, plan, use tools, interact with their environments, and execute complex tasks.

• Design experiments and evaluation approaches to measure model and system performance across real-world Physical AI scenarios.

• Deploy and optimize AI and robotics workloads on edge computing platforms such as NVIDIA Jetson.

AI Platform & Software Engineering

• Develop high-quality Python software for robotics, AI/ML, automation, and system integration.

• Build modular and reusable AI/robotics capabilities that can be consumed by other applications, agents, and enterprise AI platforms.

• Design tools and services with well-defined APIs, input/output schemas, configuration, and reusable interfaces.

• Integrate robotics and AI capabilities with agentic frameworks, orchestration platforms, enterprise applications, and external systems.

• Develop APIs, services, SDK integrations, and containerized components to move prototypes toward reusable and scalable solutions.

• Apply software engineering practices including source control, testing, documentation, configuration management, and maintainable component design.

Client Engagement & Lab Operations

• Design and deliver compelling demonstrations of robotics and Physical AI capabilities for clients and internal EY teams.

• Explain complex technical concepts clearly to both technical and business audiences.

• Participate in innovation workshops and help translate business challenges into Physical AI use cases and prototypes.

• Support the operation, configuration, maintenance, and continuous improvement of the Lab's robotics, AI, simulation, and edge-computing environments.

Required Qualifications

• Bachelor's or Master's degree in Robotics, Engineering, Computer Science, Artificial Intelligence, Machine Learning, or a related technical field.

• Understanding of robotics fundamentals including perception, localization, navigation, motion planning, kinematics, control.

• Practical experience with computer vision and/or video processing, such as object detection, segmentation, tracking, pose estimation, or scene understanding.

• Hands-on experience working with robotic platforms, sensors, robotic SDKs/APIs, or autonomous systems using edge devices.

• Experience with robotics simulation environments such as NVIDIA Isaac Sim, Gazebo, or MuJoCo.

• Hands-on experience with ROS/ROS2.

• Experience with Vision-Language Models (VLMs), Vision-Language-Action (VLA) models, imitation learning, and reinforcement learning.

• Strong Python software engineering skills with hands-on experience developing AI/ML, robotics, automation, or system-integration solutions.

• Experience breaking down real world processes into components, and translating specifications of components into physical AI functions, objectives, and constraints.

• Experience developing client-facing technology prototypes, demonstrations, or innovation solutions.

Preferred Skills & Experience

• Ability to work independently.

• Hands on experience with physical AI hardware, including motors, sensors, edge devices, and communication devices.

• Experience with modern AI/ML frameworks such as PyTorch, TensorFlow, Hugging Face, or equivalent technologies.

• Strong foundation in machine learning and data science, including data preparation, model development, training, evaluation, experimentation, and deployment.

• Experience integrating LLMs or agentic AI frameworks with robotic systems, tools, APIs, or external applications.

• Experience with Docker, Git, REST APIs, CI/CD, and modern software development practices.

• C++ experience, particularly within robotics or high-performance systems.

What Success Looks Like

The successful candidate will be able to bridge the gap between AI research, robotics, and practical software engineering. They will be comfortable moving from experimentation with a new AI model to integrating it with a physical robot, testing it in simulation and real-world environments, and packaging the resulting capability so that it can be reused by other applications and AI agents.

They will help transform the EY.ai Lab from a collection of advanced physical technologies into an integrated Physical AI experimentation and innovation environment, where robots, multimodal AI, intelligent agents, sensors, and enterprise software can work together to solve real-world problems.

Range: 88K - 132K CAD