Top 10 Physical AI Jobs for Robotics & Autonomous Systems Engineers

Key Takeaways

• Physical AI creates a distinct category of engineering roles that did not exist at scale a decade ago, spanning perception, planning, controls, and robot learning.

• Salary ranges for experienced Physical AI engineers regularly exceed $150,000 in the United States, with senior and staff roles at well-funded companies reaching significantly higher;

• You do not need a PhD to land most Physical AI engineering roles, but hands-on project experience and familiarity with ROS 2 are close to non-negotiable.

• The fastest-growing roles right now are in simulation engineering, robot learning, and perception, driven by the industry's shift toward learned robot behaviors.

The market for Physical AI jobs is growing more rapidly than even most engineering fields. Systems like autonomous cars, warehouse robots, surgical assistants, and humanoid robots need engineers to build, train, and deploy their AI. The most difficult aspect for many job seekers is the variety of fields Physical AI encompasses. Job roles, their descriptions, and the challenges to access them from a position of expertise may not be as apparent as in other fields.

This guide outlines Physical AI jobs and focuses on the job description, required skills, average salary, and how to improve your chances of becoming a successful applicant. The jobs will be both established and those created recently due to the rapid integration of machine learning in the field of robotics.

Top Physical AI Jobs Ranked

Based on our analysis of job posting volume, salary data, and hiring velocity across thousands of listings on the platform, these are the top Physical AI roles right now, ranked by a combination of demand, compensation, and career growth potential:

1. Robotics Software Engineer

2. Autonomy Engineer

3. Perception Engineer

4. Controls Engineer

5. SLAM Engineer

6. Motion Planning Engineer

7. Simulation Engineer

8. Robot Learning Engineer

9. Embedded Systems Engineer

10. Computer Vision Engineer

Each role is covered in detail below. The ranking reflects overall opportunity across the market. Individual roles may rank higher or lower depending on your specific background, location, and whether you are targeting startups, established companies, or research organizations.

Robotics Software Engineer

What the role involves: Robotics Software Engineers design and build the software that controls a robot. This involves writing support software for the hardware, building software for a robot to understand and perceive its environment, and integrating planning and control algorithms, among other tasks.

Core skills required: Python, C++, ROS 2, Linux systems programming, familiarity with sensor interfaces, debugging skills for real hardware. Experience with containerization (Docker) and CI/CD pipelines is increasingly expected.

Typical salary range (US): $120k - $180k for mid-level. Senior roles frequently exceed $200,000 in total compensation at well-funded startups and large technology companies.

Who hires for this role: Every company building a physical robot needs robotics software engineers. Volume is highest at autonomous vehicle companies, warehouse robotics firms, and humanoid robot startups.

Browse open robotics software engineer jobs currently listed on the platform.

Autonomy Engineer

What the role involves: Autonomy Engineers design and build the systems that enable robots and vehicles to operate without the help of a human. The main focus is on how a system decides what the best action to take next is, given all of its perceived environmental and situational knowledge.

Core skills required: Strong software engineering skills in C++ and Python, experience with state machines and behavior trees, understanding of probability and Bayesian reasoning, familiarity with sensor fusion, and often experience with autonomous vehicle stacks or robot middleware.

Typical salary range (US): $140k - $200k for mid-level engineers. Autonomy leads at well-funded autonomous vehicle companies often exceed $250k in total compensation.

Who hires for this role: Autonomous vehicle companies, drone companies, mobile robot manufacturers, and defense-adjacent robotics firms.

Explore current openings in autonomy engineer roles on our platform.

Perception Engineer

What the role involves: Building robots that understand their environment is the goal of perception engineering. Perception engineers sort through raw data captured by various sensors (e.g., cameras, LiDAR, etc.) to identify features like objects, locations, and even semantics. Perception engineering is one of the most challenging and specialized roles in robotics.

Core skills required: Deep learning, computer vision, sensor fusion, Python and C++, experience with frameworks like PyTorch, familiarity with 3D geometry and coordinate transforms. Experience with lidar processing libraries (Open3D, PCL) is a strong advantage.

Typical salary range (US): $130k - $195k at mid-level. Perception roles at autonomous vehicle and humanoid robot companies frequently sit at the top of the compensation range for their level.

Who hires for this role: Autonomous vehicle companies, robotics startups, agricultural automation firms, and industrial robot manufacturers.

See open perception engineer positions now hiring.

Controls Engineer

What the role involves: Module controls engineering refers to the layer that converts an abstract command into a physical action. When a control layer receives a command to move an articulated structure to a point with a specified speed, that layer of engineering would be responsible for devising a way of moving all the robot's joints and actuators to the commanded point. This requires an in-depth understanding of robot dynamics, engineering movement, and mathematically designing a system to control the behavior of the robot.

Core skills required: Control theory (PID, LQR, MPC and similar), robotics dynamics, C++ for real-time systems, MATLAB or similar for modeling and simulation, and often experience with specific hardware platforms (robot arms, mobile bases, or vehicle systems). Knowledge of ROS 2 control frameworks is increasingly standard.

Typical salary range (US): $120k - $175k at mid-level. Hardware-adjacent controls roles at automotive and aerospace companies sometimes come with additional benefits that raise total compensation further.

Who hires for this role: Robot arm manufacturers, humanoid robot companies, autonomous vehicle teams, aerospace and defense contractors, and any organization deploying robots that require precise physical motion.

Browse controls engineer jobs currently open on the platform.

SLAM Engineer

What the role involves: SLAM refers to Simultaneous Localization and Mapping. SLAM engineers create the systems that enable a robot to construct a map of an environment while determining its own location in that environment. SLAM systems are critical for the navigation of autonomous robots. SLAM is a complex engineering specialty of its own that combines many different fields.

Core skills required: Probabilistic robotics, factor graphs and optimization (GTSAM, g2o), lidar and camera-based odometry, C++ for performance-critical code, and familiarity with standard SLAM frameworks. A strong mathematics background in linear algebra and probability is essential.

Typical salary range (US): $130k - $185k at mid-level. SLAM specialization commands a premium because the skill set is genuinely rare relative to demand.

Who hires for this role: Autonomous vehicle companies, warehouse robot manufacturers, drone companies, and any firm building robots that navigate in GPS-denied environments.

See current SLAM engineer job openings on the platform.

Motion Planning Engineer

What the role involves: The systems engineers who work out how robots move are called motion planning engineers. Motion planning engineers incorporate the ideas of path planning and optimization while avoiding collisions to ensure a robot gets to its destination in the most efficient way possible. For robot arms, this means planning motions in high-dimensional joint space. For mobile robots, it means navigating through cluttered environments in real time.

Core skills required: Optimization theory, sampling-based planning algorithms (RRT, PRM), trajectory optimization libraries (CHOMP, TrajOpt), C++ for real-time performance, and familiarity with robot kinematics. Experience with the MoveIt framework for robot arms is commonly expected.

Typical salary range (US): $125k - $180k at mid-level. Motion planning specialists at humanoid robot companies are particularly well-compensated given the difficulty of planning in complex, human-scale environments.

Who hires for this role: Robot arm manufacturers, humanoid robot companies, autonomous vehicle teams, and surgical robotics firms.

Explore motion planning engineer roles currently hiring.

Simulation Engineer

What the role involves: Simulation Engineers design and manage the virtual spaces that enable the training, testing, and validation of Physical AI systems deployed in the Real World. Now that the sim-to-real paradigm has developed, most Physical AI companies have Simulation Engineers as a key role in their development process. Simulation Engineers are at the junction of software engineering, physics, and machine learning.

Core skills required: Experience with simulation platforms (Isaac Sim, Gazebo, MuJoCo, Webots), Python and C++, 3D asset creation or integration, understanding of physics simulation, and increasingly, experience with domain randomization techniques used to improve sim-to-real transfer.

Typical salary range (US): $115k - $170k at mid-level. Simulation engineering has seen faster salary growth than most other Physical AI roles over the past two years as demand has outpaced supply.

Who hires for this role: All major Physical AI companies with active training pipelines. Demand is especially high at humanoid robot companies, autonomous vehicle firms, and organizations working on foundation models for robotics.

The MuJoCo physics engine (dofollow), now maintained by DeepMind, is one of the most widely used simulation platforms in research and increasingly in production environments. Familiarity with it is a genuine differentiator for simulation engineering candidates.

Browse open simulation engineer positions on our platform.

Robot Learning Engineer

What the role involves: Robot Learning Engineers focus on implementing machine learning methodologies to program robots with new skills and tasks. These skills may come from training with human demonstrations (imitation learning), reinforcement learning in virtual environments, or transference of tasks learned by interacting with a virtual environment to a physical one. This is one of the newest and fastest growing occupations due to the industry's adoption of learned tasks over programmed robot behaviors.

Core skills required: Deep reinforcement learning, imitation learning, PyTorch, experience with robotics simulation environments, understanding of sim-to-real transfer challenges, and Python. Experience with large-scale training infrastructure is increasingly valued as models grow larger.

Typical salary range (US): $140k - $210k at mid-level, with senior roles at research-oriented companies reaching higher. This is currently one of the highest-compensating Physical AI roles, reflecting genuine scarcity of engineers with both ML depth and robotics deployment experience.

Who hires for this role: Humanoid robot companies, research labs, autonomous vehicle teams with research divisions, and any organization working on generalist robot policies or foundation models for robotics.

Robot Learning Engineer is the role we have seen the fastest salary growth for over the past 18 months; Companies competing for engineers who can bridge deep reinforcement learning with real hardware deployment are consistently offering compensation packages that exceed those for equivalent-level software engineering roles at the same organization. - Physical AI Jobs

Embedded Systems Engineer

What the role involves: Embedded Systems Engineers work at the intersection of hardware and software. They program the low-level code and firmware that run on the microcontrollers and embedded processors inside of robots. This involves the development of drivers for both the sensors and actuators, development of RTOS tasks, and ensuring the robotic system fulfills the strict requirements of real-world robotics in terms of latency and reliability.

Core skills required: C and C++ for embedded systems, RTOS (FreeRTOS, Zephyr), familiarity with communication protocols (CAN bus, EtherCAT, SPI, I2C), hardware debugging tools (oscilloscopes, logic analyzers), and often experience with specific processor families (ARM Cortex-M, FPGA basics).

Typical salary range (US): $110k - $160k at mid-level. Embedded roles tend to pay slightly less than pure software roles at the same experience level, but the hardware depth makes these engineers difficult to replace.

Who hires for this role: Robot hardware companies, industrial automation manufacturers, defense contractors, and any organization building custom robotics hardware.

Computer Vision Engineer

What the role involves: Computer Vision Engineers create and develop the systems for processing images and videos that enable robots and other autonomous systems to interpret the Visual world. While perception engineers work with various modalities of sensors, computer vision engineers are specialized in camera-based perception of tasks such as detection, tracking, depth estimation, segmentation, and visual localization.

Core skills required: Deep learning for vision (CNNs, ViTs, detection architectures like YOLO and DETR), OpenCV, PyTorch, camera calibration and intrinsics, stereo vision and depth estimation. Experience with real-time inference optimization (TensorRT, quantization) is increasingly expected in production roles.

Typical salary range (US): $125k - $185k at mid-level. Computer vision engineers with robotics deployment experience, as opposed to pure vision research experience, command a premium because the production constraints differ significantly from academic benchmarks.

Who hires for this role: Autonomous vehicle companies, agricultural robotics firms, medical imaging and surgical robotics companies, and any organization relying heavily on camera-based sensing.

What Skills Do You Need for Physical AI Jobs?

Across all of the roles above, a consistent set of foundational skills appears, These are the ones that appear most frequently across job postings on our platform and that hiring managers consistently cite in conversations about what separates strong candidates from weak ones.

Python and C++: Python dominates AI and ML development. C++ is required for performance-critical systems, hardware interfaces, and real-time code. Most Physical AI roles expect both.

ROS 2: The Robot Operating System (ROS 2) is the standard middleware for robotics development; It appears in the majority of robotics job postings across all role types. Ignorance of ROS 2 is a significant disadvantage in most Physical AI hiring processes.

Linear algebra and probability: The mathematics underpinning Physical AI, from sensor fusion to motion planning to robot learning, requires genuine comfort with matrix operations, probability distributions, and optimization. These are not optional backgrounds.

Simulation tools: Experience with at least one simulation environment (Isaac Sim, Gazebo, MuJoCo, Webots) is increasingly standard, not optional.

Git and software engineering fundamentals: Physical AI is production engineering. Code review, version control, testing, and CI/CD practices are expected at every level above entry.

For a full breakdown of skills by role type, see our guide on what skills you need for a robotics career. For engineers looking to build credentials through structured learning, Coursera's robotics programs (dofollow) and edX's robotics courses (nofollow) both offer structured paths from foundations to applied skills, including programs from leading universities.

What Career Paths Exist in Physical AI?

Physical AI careers generally develop along one of three tracks, with meaningful movement between them possible as experience grows.

The generalist engineering track starts with a robotics software engineer role and broadens over time. Engineers on this path accumulate experience across perception, planning, and controls, and often move into technical lead or architect roles overseeing the integration of full robot systems.

The specialist track goes deep in a single domain: perception, controls, SLAM, or motion planning. Specialists at the senior level are highly valued and difficult to hire. The tradeoff is that depth comes at the cost of breadth, and specialists may find it harder to move laterally across role types.

The research-to-engineering track starts in a research context, either at a university or a company research lab, and transitions toward applied engineering as technology matures into products. Robot Learning Engineers often follow this path.

Physical AI career paths are less linear than traditional software engineering. The most successful engineers we track move fluidly between depth & breadth across their careers: spending two to 3 years going deep in one specialty, then broadening their scope before specializing again at a higher level. This pattern is consistent across candidates who reach staff and principal engineer levels. - Physical AI Jobs

For engineers making a transition from software backgrounds, our guide on transitioning from software engineering to robotics lays out a practical, step-by-step path. For those questioning whether a degree is necessary, our analysis of whether you need a degree for robotics careers is worth reading before making assumptions.

Safety and standards are also an important dimension of Physical AI careers that rarely gets enough attention. The ISO robotics safety standards (nofollow) govern how physical robots must be designed and tested in many commercial and industrial contexts, and engineers working in regulated applications need familiarity with these frameworks.

How Do You Get Started in Physical AI?

The path into Physical AI is more accessible than the technical depth of the roles might suggest; Most entry-level Physical AI engineers are not PhD graduates. They are engineers with strong fundamentals, relevant project experience, and demonstrated ability to work with physical hardware.

Step 1: Build a foundation. If you already write Python and C++ competently and understand basic linear algebra, you have the starting point. If not, those are the first gaps to close.

Step 2: Learn ROS 2. Work through the official tutorials with a physical or simulated robot. Build at least one project that uses ROS 2 to connect a sensor to an algorithm to an output.

Step 3: Build something physical. A project that runs on real hardware, however simple, is worth more than a dozen Python notebooks on your resume. Even a basic mobile robot navigating a room is a meaningful credential. Our guide on how to build a robotics portfolio covers how to document and present these projects effectively.

Step 4: Choose a specialization. Pick one of the roles above that aligns with your background and interests. Go deep on the core skills for that role. Breadth comes later; employers value focus at the entry level.

Step 5: Apply strategically. Research which companies are actively building Physical AI systems and which regions have the strongest hiring markets.

Frequently Asked Questions

What are the highest-paying Physical AI jobs?

Robot Learning Engineer, Autonomy Engineer, and Perception Engineer are among the highest-paying Physical AI roles. Senior positions at well-funded US companies can exceed $200,000 in total compensation, while staff and principal roles can reach $250,000 or more.

Which Physical AI job is easiest to get into?

Robotics Software Engineer is often the most accessible entry point for software engineers. Strong Python, C++, and ROS 2 skills are important, without requiring the deeper specialization needed for areas like SLAM or controls. Simulation Engineer is another accessible path for candidates with 3D tooling experience.

Do I need a PhD for Physical AI jobs?

No. Most Physical AI engineering roles do not require a PhD. A strong portfolio, relevant coursework, and hands-on experience with ROS 2 and simulation tools are often sufficient. PhDs are more common for research scientist and robot learning positions.

What is the average Physical AI salary?

Mid-level Physical AI engineering roles in the US typically offer $120,000 to $180,000 in base salary. Total compensation can be higher with equity and bonuses, while entry-level roles often start around $90,000 to $115,000. See our full breakdown of the highest-paying robotics careers for a role-by-role analysis.

Which Physical AI skills are most in-demand?

Based on our analysis of current job postings, the most consistently requested skills include ROS 2, Python, C++, computer vision, deep learning, and simulation. Sensor fusion and reinforcement learning are also growing skill requirements.

Can I get a Physical AI job without a robotics degree?

Yes. Many Physical AI engineers come from mechanical engineering, electrical engineering, computer science, or mathematics backgrounds. What matters most is demonstrable technical skill and hands-on experience with physical systems. Our guide on getting started in robotics without a degree covers this in full.