How The Robotics Engineer Skill Set Is Changing In The Age Of Physical AI

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By Jakub Tomaszewski, senior robotics engineer at SoftServe

My path as a robotics engineer started in 2020, and while my introduction to the world of robotics wasn't that long ago, the work I've done and the industry itself has permuted and evolved so much in just six years.

Back then we wrote every line of code by hand, teammates reviewed it in detail, and issues took longer to fix. Stack Overflow was often the first place to look for a solution, and avenues for learning spanned across books, research papers, Google, and YouTube.

Today, AI can accelerate much of that work. Writing code, diagnosing problems, and learning more about an unfamiliar concept can all happen faster. But that does not necessarily make the senior engineer's role simpler.

As implementation becomes easier to accelerate, more weight shifts toward understanding the whole system: deciding how it should be designed, integrating its parts, validating whether it works, and knowing when an apparently correct solution is actually wrong.

That is why I think the definition of a robotics engineer is changing. In this article, I will explain what skills and foundations matter most to senior robotics engineers in 2026.

What skills matter for senior robotics engineer in 2026

Robotics has always combined different disciplines, but the level of their integration is much higher now. Better hardware acceleration, more capable simulation environments, and AI are changing both what we build and how we build it. I see four areas that have either expanded significantly or become much more important.

1. Interdisciplinary knowledge and system integration

These skills have expanded a lot because robotics increasingly connects mechanics, electronics, control, perception, software architecture, and validation.

At the same time, AI and hardware acceleration are shifting senior, lead, and architect roles toward architecture, integration, and validation rather than isolated implementation tasks.

This matters when building complete systems. For example, a digital twin requires a 3D model of the robot, a physics description, sensor configuration, and a telemetry path from the real machine.

2. Simulation

Simulation has moved from a supporting activity to a core engineering method. We use it to design, test and lower risks before we even touch machines.

This became more important because physical testing is slower, costly, and sometimes much less safe, while computing power and simulation tools have improved significantly.

High-fidelity simulation can be used to study complex robotic systems before committing to hardware, while co-simulation allows us to connect a high-fidelity model of a nonlinear system with a robotics simulator such as Nvidia Isaac Sim.

3. Physical AI and reinforcement learning

Physical AI and reinforcement learning have moved from research closer to daily engineering practice as robots are expected to operate in changing environments.

Reinforcement learning can make a controller more robust in changing and stochastic conditions, while Physical AI expands the role of AI from software-only tasks to systems that perceive, act, and interact with the physical world.

For a senior engineer, this means understanding how to mix learning-based approaches with classical robotics methods instead of keeping them separate.

4. Synthetic data and digital twins

These competence areas have become more relevant because collecting real-world data and testing on physical systems can be expensive and time-consuming.

Synthetic data can accelerate computer vision and imitation learning when labeled real data is scarce, while digital twins make it possible to model and study a robotic system before or alongside work with the real machine. Both depend on strong integration between simulation, models, sensors, and the physical system.

Start with a problem

For an experienced engineer, the most effective approach is to start with a concrete technical problem rather than trying to learn a new field in isolation.

Set a clear goal, identify the knowledge gaps, and use the most relevant resources, such as research papers, technical documentation, discussions with colleagues, and AI to navigate the vast amount of available information and accelerate the learning process.

The important part is to apply the new knowledge immediately in engineering work or laboratory experiments. This makes it possible to test whether you actually understand the concept and where the remaining gaps are.

What foundations remain essential for a senior robotics engineer?

Mathematics and robotics theory remain essential. I find it difficult to imagine a strong robotics engineer who has not spent thousands of hours on the basics.

If someone claims that AI can replace that foundation, I would answer that it can, until it cannot. Each day I correct AI when it writes mathematics or implements a robotics concept in the wrong way.

Those fundamentals are also the building blocks of architecture. A robotic system is an assembly of kinematics, dynamics, estimation, control, perception, and software interfaces.

If an engineer cannot judge whether a model is well posed, whether a controller is stable, or whether a sim-to-real gap is acceptable, architecture becomes a drawing without physics. Tools change; the need to reason about the plant does not.

For me, that foundation came through specialized university education and then continued through practice and self-study. For my engineering thesis, I designed and constructed a manipulator from scratch.

Later, during my master's studies in Embedded Robotics, I continued working with more advanced robotics problems. I still return to mathematical models, derivations, and research papers when a project demands it.

I also started a PhD focused on fusing classical control theory with AI. It forces me to keep the theory sharp while exploring how classical control and newer AI methods can work together in practice.

How has seniority in robotics evolved in recent years?

Seniority used to be described mainly through hard skills like programming languages, frameworks, and years of delivery.

Those still matter, and at SoftServe, robotics roles continue to ask for strong mathematics, C++ or Python, ROS2, simulation tools such as Isaac Sim or Gazebo, and the ability to test both in simulation and on hardware. But today I would not define a senior robotics engineer by a technology checklist.

For me, seniority is defined as much by professional behavior as by technical expertise. In recent years, the senior bar has moved from implementing a component well toward owning a system, integrating it, validating it, and communicating the result. Characteristics and capabilities a senior robotics engineer should encompass include:

Communication and decision-making. You need to explain architecture to a project manager, a client, and leadership, often with different levels of technical background. You should be able to propose a decision, defend it with evidence, and still listen when somebody challenges it. Self-discipline and continuous learning. The domain does not pause. Experts must adapt to new frameworks, simulators, and AI methods while delivery dates become shorter and expectations around AI acceleration grow. Ownership and engineering judgment. Robotics combines uncertainty in hardware with pressure on time. A senior engineer should be able to finish a task or a project without close guidance, make trade-offs, and keep the quality of the work. Interdisciplinary experience. The senior role is built by working across mixed areas of expertise, such as digital twins, multi-robot co-simulation, underwater vehicles, industrial manipulators, drones, and laboratory hardware. That variety teaches where a method is applicable and where it is not, as well as how to collaborate with specialists from neighboring fields. System-level responsibility. AI has accelerated the implementation part of engineering. It has not removed the need to own the system, integrate it, validate it, and communicate the result.

Why working with hardware matters for a senior robotics engineer

For all the changes in how robotics engineers work today, hardware remains where engineering judgment is tested. Hardware teaches you what simulation cannot: which assumptions survive contact with the real world.

Sensors have noise, cameras have imperfect calibration, actuators have limits and delays, and every robot has mass, inertia, friction, and flexibility that a simulator may simplify.

Simulation is still invaluable for designing architectures and trajectories, running exploratory studies, and testing scenarios that are unsafe or difficult to reproduce in the lab.

In our team, we combine this simulation-first approach with hands-on work in our Robotics Labs in Lviv and Wrocław. Engineers can move from prototypes to integrated systems and validate them on real hardware, including humanoids, robodogs, industrial robotic arms, and other autonomous systems.

This physical access matters because before going from sim to real, we need model identification and validation on hardware, where the remaining gaps become visible.

That, for me, comes back to what it means to become a senior robotics engineer today. AI can accelerate coding, simulation can accelerate testing, and new tools can shorten development cycles, but someone still has to understand the system well enough to know whether the result will work outside the virtual environment.

About the author: Jakub Tomaszewski is a senior robotics engineer at SoftServe. He specializes in simulation, digital twins, co-simulation, advanced control, and Physical AI. He is also a PhD student at Wrocław University of Science and Technology, where he studies the fusion of classical control and AI for wheeled bipedal robots.

Main image via Easy-Peasy

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