Physical AI Jobs for Robotics and Autonomous Systems Engineers

Key Takeaways

• Physical AI jobs span robotics, autonomy, perception, controls, simulation, and robot learning, each a distinct engineering discipline with its own skill set and salary range.

• Demand for Physical AI engineers has grown sharply since 2023, driven by investment in humanoid robots, autonomous vehicles, and AI-powered industrial automation;

• Mid-level Physical AI engineers in the United States earn between $120k and $195k in base salary, with senior roles at well-funded companies exceeding $200,000 in total compensation.

• Hands-on project experience and ROS 2 proficiency consistently outweigh academic credentials at most hiring companies.

Physical AI refers to the engineering science used in creating machines that can sense, think, and react to the physical world. This field includes everything from the robot arm picking packages at a fulfillment center to the self-driving truck driving down the highway to the robot in surgery helping the surgeon perform his tasks.

This page will help you find Physical AI jobs and learn more about what is involved in each. Below, you can see job listings, categories of roles, and helpful guides and resources on this page.

What Is Physical AI?

The term Physical AI is applied to describe artificial intelligence implemented in machines that function in the physical world. Physical AI systems receive information from actual sensory devices, like cameras, laser rangefinders, and touch sensors; they process that information to understand their environment and respond to it through physical action. A robot that grabs an item, a car that stops for a pedestrian, or a drone that changes course due to an obstacle: all of these are Physical AI examples.

The term covers a cluster of closely related disciplines. Robotics provides the hardware and control foundations. Machine learning provides the models that allow systems to perceive, adapt, and learn. Autonomous systems bring together perception, planning, and action into agents that operate without constant human input. ETH Zurich's Robotics research division describes the core challenge of Physical AI well: building systems that are reliable, safe, and capable enough to operate in environments they were not explicitly programmed for.

For a deeper explanation of what Physical AI means and how it differs from traditional software AI, see our guide on what Physical AI is and how it works.

Physical AI Job Categories

Physical AI is not one particular job position; rather, it is a whole industry that creates opportunities for a variety of engineering specialties. Determining which specialty is appropriate for you is the first step to taking up the right job.

Robotics Software Engineering covers the full software stack running on a robot:- sensor drivers, middleware, planning integration, system debugging & deployment. It is the most generalist Physical AI role and the most widely posted across the industry. Engineers with strong Python, C++, and ROS 2 skills are the core hiring target.

SLAM Engineering (Simultaneous Localization and Mapping) is the specialty of helping robots know where they are while building a map of where they have been; It is fundamental to any mobile robot and is one of the most mathematically demanding specializations in the field.

Motion Planning Engineering covers the algorithms that calculate how a robot moves from one configuration to another without hitting anything and within the physical limits of its hardware. It involves optimization, sampling-based planning, and trajectory generation.

Autonomy Engineering focuses on decision-making:- how a robot or vehicle decides what to do next given everything it knows about its environment; Autonomy engineers work on behavioral planning, mission execution logic, and the systems that tie perception and motion planning together into a coherent agent.

Perception Engineering is the discipline of making machines understand what they see & sense. Perception engineers process raw data from cameras, lidar & radar into useful representations: detected objects, tracked positions, semantic maps of the environment. This role requires deep knowledge of computer vision and sensor fusion.

Controls Engineering handles the layer between decisions and physical motion; When a planner says "move to position X," the controls system determines exactly how much current to send to each motor, at what timing, to make that happen reliably. It requires control theory, robotics dynamics & real-time systems experience.

Simulation Engineering builds and maintains the virtual environments used to train & test Physical AI systems before they touch real hardware. As sim-to-real pipelines have matured, simulation engineers have become core members of almost every Physical AI development team.

Robot Learning Engineering applies machine learning directly to robot behavior: training policies from human demonstrations, running reinforcement learning in simulation, and transferring learned behaviors to real hardware. It is the fastest-growing role category in the field.

Browse all open roles by category:

• Robotics engineering jobs

• Autonomy engineer jobs

• Perception engineer jobs

• Controls engineer jobs

Robotics Engineering Roles: What Each One Pays

Salaries happen to be the most searched-for topic among engineers interested in Physical AI jobs. The following statistics have been compiled from our analysis of salary data available from job postings, individual salaries, and disclosed offers in the Physical AI field.

Figures represent base salary in USD. Total compensation including equity and bonuses is typically 20 to 40 percent higher at well-funded startups and large technology companies. For current market data, Glassdoor's robotics engineer salary data provides a useful benchmark for role-by-role comparisons.

Across the Physical AI job listings we track, Robot Learning Engineer and Autonomy Engineer have shown the fastest base salary growth over the past 18 months, averaging year-over-year increases of 12 to 18 percent at mid-level. This reflects genuine scarcity: the pool of engineers who combine strong ML fundamentals with real robotics deployment experience remains small relative to industry demand. - Robotica Network

For a full role-by-role breakdown of compensation and career trajectories, see our dedicated guide on the highest-paying robotics and Physical AI careers.

What Skills Do Physical AI Jobs Require?

The skill set for Physical AI positions does not really vary too much between different types of jobs that need to be filled, no matter which one is in question. The following are skills that can be found in the majority of listings on our site.

Python is the go-to programming language for AI and ML applications in Physical AI. All jobs require it. It is used for writing training pipelines, processing data from sensors, experimenting, and making tools.

C++ is necessary for high-performance applications, hardware-level tasks, and all things that require very low latencies. Roles related to controls, SLAM, and embedded systems tend to require a lot of C++.

ROS 2 (Robot Operating System 2) is the standard middleware that connects software components in a robot system. It handles communication between sensors, algorithms, and actuators. Familiarity with ROS 2 is listed in the majority of robotics job postings across all specializations. The ROS Discourse community forum is one of the most active technical communities for engineers working with ROS 2 in production environments.

Both computer vision and deep learning are mandatory requirements for perception, robot learning, and autonomy positions. Knowledge about convolutional neural networks, architectures of object detection, and vision transformers becomes more of a basic requirement than a distinctive feature nowadays.

Knowledge about linear algebra and probability forms the basis of most algorithms implemented by Physical AI developers. The engineers who lack knowledge in these domains face an obstacle at the very beginning of their work.

Simulation platforms like Isaac Sim, Gazebo, MuJoCo & Webots are widely used in the work of Physical AI engineers nowadays. Proficiency with at least one is expected from mid-level and above candidates.

Sensor fusion is a way to combine information from several sensors, for instance, cameras, lidar, and IMUs, to get a more accurate picture of the surrounding world than each of these sensors could provide.

For a complete skills breakdown by role type, read our guide on what skills you need to work in robotics and Physical AI.

Physical AI Jobs' analysis of career trajectory data shows that engineers who reach staff or principal level in Physical AI share a consistent pattern: they spent their early careers going deep in one technical area, then deliberately broadened their scope by working across team boundaries. - Physical AI Jobs

For those earlier in the journey, our guide on how to land entry-level Physical AI and robotics jobs explains exactly what hiring teams look for at the junior level. For engineers questioning whether they need a degree to enter the field, our analysis at do you need a degree for robotics careers gives a clear, data-backed answer.

Where Are Physical AI Jobs Located?

The hiring of physical AI is primarily done in a few but increasing number of countries around the world. The United States tops the list when it comes to the volume of jobs. In the United States, the leading locations are California, comprising the Bay Area, Los Angeles, and San Diego; Texas, consisting of Austin and Houston; and Washington. In Germany, Japan, and South Korea, there are huge ecosystems of industrial robotics that are slowly incorporating AI technology.

According to data published by the International Federation of Robotics, global robot installations have grown consistently year over year, with Asia and North America accounting for the largest shares of new deployments. Every new robot installation creates downstream demand for engineers to build, deploy, and maintain the AI systems that run on it.