More companies are building real-world robots that can see and think, so the need for engineers in Physical AI is growing fast. This job board is here for engineers, researchers, and technicians searching for roles in robotics, AI, and autonomous systems.
What is Physical AI?
Physical AI focuses on robots that work in the real world. These robots are able to be fully autonomous and assist humans in performing surgery and lifting.
The key distinction between Physical AI and AI is that Physical AI focuses on the real world. The limitations for standard AI are to train and write using clean data and text. AI that can see and think has to analyze the real world through sensors and cameras. This AI has to make decisions and perform actions such as move and grasp in mere milliseconds. If this AI makes a mistake, it could endanger people and the environment.
This branch of AI is extremely hard to perform since the real world is not orderly like databases. Engineers who can develop AI using Physical AI to perform in the real world are in extremely high demand.
Read our full guide on what Physical AI is and how it works, or explore how the terminology breaks down in our guide on Physical AI vs embodied AI.
Physical AI by the Numbers
| Metric | Data Point |
|---|---|
| Global robotics market size (2024) | $73 billion |
| Projected market size (2030) | $165 billion+ |
| Average mid-level Physical AI salary (US) | $125,000 to $185,000 |
| Fastest-growing role | Robot Learning Engineer |
| Top hiring region --- North America | United States (California, Texas, Pacific Northwest), Canada (Toronto, Vancouver, Waterloo) |
| Top hiring region --- Europe | Germany (Munich, Stuttgart), Switzerland (Zurich), Netherlands (Eindhoven, Delft), Sweden (Stockholm, Gothenburg), UK (London, Cambridge) |
| Top hiring region --- Asia | Japan (Tokyo, Nagoya), South Korea (Seoul, Daejeon), Singapore, China (Beijing, Shanghai, Shenzhen), Taiwan (Taipei, Hsinchu) |
| ROS 2 listed in job postings | Over 70% of robotics software roles |
| Time to fill a senior SLAM role | 60 to 90 days on average |
| Countries with active Physical AI hiring | 20+ tracked on this platform |
Physical AI Job Categories
Physical AI is not a single job. It spans nine specialized engineering disciplines, each with its own skill set, career path, and salary range.
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Robotics Engineering - builds and maintains the full software stack running on robots. The largest category by job volume on the platform. Mid-level salary: $120K - $175K.
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Embodied AI - teaches robots to learn new skills from experience using techniques like imitation learning and reinforcement learning. One of the fastest-growing specializations. Mid-level salary: $150K - $220K.
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Simulation Engineering - builds the virtual environments where robots are trained before real-world deployment. Demand has grown over 40% in two years as sim-to-real pipelines mature. Mid level salary: $120K - $165K.
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ROS 2 Engineering - roles centred on ROS 2, the standard software framework for modern robotics. Appears in over 70% of robotics software job postings globally. Mid level salary: $120K - $170K.
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Autonomy Engineering - gives machines the ability to make decisions and act without human control. Core discipline in self-driving vehicles and mobile robots. Mid-level salary: $150K - $200K.
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Perception Engineering - turns raw sensor data from cameras and lidar into useful information: objects, distances, and movement. Open SLAM positions take 60 to 90 days longer to fill than most roles. Mid-level salary: $150K - $200K.
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Controls Engineering - translates high-level decisions into precise motor commands. Requires both classical control theory and modern software skills. Mid level salary: $125K - $170K.
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SLAM Engineering - helps robots figure out where they are while simultaneously building a map of their surroundings. One of the most mathematically demanding and scarce specializations. Mid level salary: $140K - $185K.
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Motion Planning - calculates how a robot moves from A to B safely and efficiently, avoiding obstacles in real time. Critical in both robot arms and mobile robots. Mid level salary: $135K - $180K.
Salary Snapshot by Role
| Role | Entry Level (US) | Mid Level (US) | Senior Level (US) |
|---|---|---|---|
| Robot Learning Engineer | $120K to $150K | $160K to $220K | $230K to $300K+ |
| Perception / Autonomy Engineer | $100K to $130K | $150K to $200K | $200K to $270K |
| SLAM Engineer | $100K to $125K | $140K to $185K | $190K to $250K |
| Motion Planning Engineer | $100K to $125K | $135K to $180K | $185K to $245K |
| Robotics Software Engineer | $90K to $115K | $120K to $175K | $180K to $240K |
| Controls Engineer | $90K to $115K | $125K to $170K | $175K to $235K |
| Simulation Engineer | $85K to $110K | $120K to $165K | $165K to $220K |
| Robotics Technician | $45K to $70K | $70K to $95K | $95K to $130K |
Robotics Career Paths
Physical AI careers develop along three main tracks, and movement between them is common as experience grows.
The Engineering Track is the most common path, Engineers build the systems that run in production. This track is accessible to candidates with relevant degrees and strong hands-on project experience, without requiring a PhD. Most of the job volume on our platform sits on this track.
The Research Track leads through academia or dedicated research divisions, Researchers work on unsolved problems and publish their findings. Entry typically requires a Master's or PhD. Compensation at the senior end is among the highest in the industry.
The Applied Science Track bridges research and production, Applied scientists take research results and make them work reliably at scale. This role has grown significantly as companies move from early prototypes to deployed products.
For engineers switching from adjacent fields, our guide on transitioning from software engineering into robotics lays out the practical path. If you are just starting out, our guide on getting your first entry-level robotics job covers what hiring teams actually look for.
Hiring by Region
| Region | Key Hubs | Primary Sectors |
|---|---|---|
| USA | San Francisco, Austin, Pittsburgh, Seattle | Humanoid robots, AV, warehouse automation |
| Germany | Munich, Stuttgart, Berlin | Industrial robotics, automotive |
| Japan | Tokyo, Nagoya, Osaka | Industrial, humanoid, consumer robotics |
| South Korea | Seoul, Daejeon | Industrial, semiconductor, consumer robots |
| UK | London, Cambridge, Bristol | AV, defence, research |
| Canada | Toronto, Vancouver, Waterloo | AV, AI research, industrial |
| Singapore | Singapore City | Logistics, manufacturing, research hub |
| Netherlands | Eindhoven, Delft | Industrial, agri-robotics, research |
Skills for Physical AI Roles
The skills required in Physical AI vary by role, but a consistent foundation appears across most job postings.
Python and C++ are the two dominant languages, Python is used for machine learning, scripting and rapid development. C++ is used for performance-critical systems, hardware interfaces, and real-time code. Most engineering roles expect both.
ROS 2 is the standard middleware for modern robotics, It appears in the majority of robotics job postings across all role types. Fluency in ROS 2 is close to a baseline requirement for software-focused roles.
Machine Learning and Deep Learning are increasingly expected even in roles that are not primarily ML-focused. Perception, planning, and robot learning all rely on neural network models.
Simulation Tools such as Isaac Sim, Gazebo, and MuJoCo are standard parts of the Physical AI development pipeline. Engineers who can build simulation environments have a practical hiring advantage.
Mathematics underpins almost everything in Physical AI, Linear algebra, probability, and optimization are the foundations of the algorithms used in SLAM, motion planning, controls, and machine learning.
For a full breakdown by role, see our guide on what skills you need for a robotics career. For engineers building credentials through structured learning, our guide on which robotics certifications are worth pursuing covers the programs that actually matter to hiring teams.
The skill combination that appears most consistently in successful Physical AI candidates is Python plus ROS 2 plus at least one hands-on hardware project. Engineers who have all three get interviews at a significantly higher rate. - Robotica Network
Latest Job Trends
The Physical AI job market is moving fast. Here is where hiring is concentrated right now.
Humanoid robots are the fastest-growing hiring segment. Companies like Figure AI, Agility Robotics, 1X, and Apptronik are scaling from prototype to deployment and hiring aggressively across engineering, research, and operations.
Robot learning is creating a new category of high-paying roles. As companies shift from hand-coded robot behaviors to trained policies, demand for engineers who can apply reinforcement learning to real hardware has grown sharply.
Simulation engineering is maturing into a core discipline. Simulation used to be a supporting function; Now it is central to how Physical AI companies train, test, and validate their systems.
Compensation is rising across the board. As talent demand outpaces supply, salaries for specialized Physical AI roles have risen consistently; Senior engineers in perception, SLAM, and robot learning regularly earn above $200k in total compensation at well-funded companies.
Broader context on where the industry is heading is tracked by MIT Technology Review and Ars Technica's robotics coverage. Academic research driving the next wave of capabilities is presented at ICRA, the IEEE International Conference on Robotics and Automation. Physical AI professionals and employers also connect through the Physical AI talent and intelligence infrastructure at Robotica Network and find roles through Physical AI jobs and robotics careers on Robotica Network.
Physical AI is not a niche. It is the next major platform shift in technology, & it is already creating a distinct category of engineering roles that did not exist at scale 5 years ago. The engineers who build these skills now are positioning themselves for the most consequential work in the industry. - Physical AI Jobs
Frequently Asked Questions
What is Physical AI?
AI that controls machines operating in the real world: robots, autonomous vehicles, surgical systems, and humanoid robots. Unlike software AI, it must sense, decide, and act physically in real time.
What types of Physical AI jobs are available?
Nine main specializations: robotics software, perception, autonomy, controls, SLAM, motion planning, simulation, robot learning, and embedded systems. Browse all Physical AI job listings.
What skills do I need for Physical AI roles?
Python, C++, and ROS 2 are the baseline. Most roles also expect machine learning fundamentals, simulation experience, and hands-on hardware project work.
What is the average salary for Physical AI jobs?
Entry level: $70K - $115K. Mid level: $125K - $185K. Senior level: $200K and above. Robot Learning and SLAM engineers sit at the top of the range.
Are remote Physical AI jobs available?
Mostly no. Most roles require hardware access. Simulation and ML infrastructure roles sometimes offer hybrid. See our analysis of remote work in robotics for specifics.
Which companies are hiring Physical AI engineers?
Figure AI, Agility Robotics, 1X, Waymo, Aurora, Boston Dynamics, ABB, and FANUC are among the most active. See the full list in our guide on robotics companies hiring.