About the role
About TACTILIA
At TACTILIA, we are building the industry-ready robotic hand that closes one of the biggest gaps in Physical AI. Spun out of SCHUNK, the global market leader in gripping technology, we combine a decade of robotic-hand expertise and real industrial access with the speed and ambition of a deep-tech startup.
This isn't a research project waiting for its first customer. We already have a product, customers are ready to use it, and we are launching now. Your work will directly shape the electronics, actuators, and embedded systems that make that possible.
You will join at the moment when the hard engineering questions become real product decisions: how do we make a highly capable robotic hand reliable, manufacturable, serviceable, and simple enough to deploy on a real shop floor?
The Role:
As an AI Software Engineer focused on Reinforcement Learning, you will develop the learning systems that enable TACTILIA's robotic hands to acquire complex manipulation skills through interaction and experience.
You will work across reinforcement learning, robotics and simulation to develop policies that can learn complex behaviours and ultimately perform them reliably on real hardware. Your work will help push dexterous manipulation beyond hand-engineered behaviours towards scalable, adaptive robotic skills.
What you will do:
Develop reinforcement learning algorithms for dexterous manipulation and robotic hands
Design training environments, reward functions and learning curricula for complex manipulation tasks
Train and evaluate policies in simulation and on real robotic hardware
Develop methods for efficient exploration, learning and policy optimisation
Work on sim-to-real transfer and robustness of learned policies
Build software infrastructure for large-scale training, evaluation and experimentation
Analyse policy behaviour and failure modes and use these insights to improve training
Integrate learned policies with robotics and control systems
Work closely with simulation, controls and robotics engineers to bring learned behaviours onto the real hand
Contribute to scalable approaches for learning new manipulation skills
What you bring:
Degree in Computer Science, Robotics, AI, Electrical Engineering or a related field
Strong software engineering skills in Python and preferably C++
Hands-on experience with reinforcement learning and deep learning
Strong understanding of RL concepts such as policy optimisation, value functions, exploration and reward design
Experience with PyTorch or a similar deep learning framework
Experience working with robotic systems and simulation environments
Good understanding of robot kinematics, dynamics and control
Strong analytical and problem-solving skills
A hands-on mindset and enthusiasm for testing learning algorithms on real robotic systems
Bonus
Experience with dexterous manipulation, grasping or robotic hands
Experience with sim-to-real reinforcement learning
Experience with PPO, SAC or other modern RL algorithms
Experience with Isaac Lab, Isaac Sim, MuJoCo or similar environments
Experience with distributed or large-scale RL training
The next chapter of robotics won't be built in a lab. It will be built by people who care about what happens when technology meets the real world.
At TACTILIA, you'll work on one of the hardest open problems in robotics: giving machines the dexterity, reliability and robustness to interact with the physical world at industrial scale.
The technology is taking shape, the team is being built, and the standards around robotic hands and their skills are still open. That means your decisions will have a lasting impact — not just on a product, but on what comes next for Physical AI.
Build the hand. Define the standard. Shape what comes next.
If you're excited by the challenge of building robotic hands that works reliably in the real world, we'd love to hear from you.
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