This is a career-changing opportunity to work alongside world-class engineers and scientists on a mission to reimagine what machines can do. You will help us build the digital nervous system of intelligent agents, not by refining existing tools, but by inventing new systems entirely. Your work will directly shape how intelligent machines learn, adapt, and act in the physical world.
We are now extending our models into robotics, where they act as an adaptive layer between higher-level learning systems, such as reinforcement learning (RL) and vision-language-action (VLA) models, and low-level actuators. This layer can significantly reduce the sim-to-real gap and address the core deployment challenges of the real world in general-purpose robotics. The systems you help design will be exercised against real-world constraints and tested on real robotic platforms.
To do this exceptionally well, we need deeper, hands-on understanding of the limitations of RL in real systems. You will look into how our model interacts with RL policies in practice and help quantify Intui’s capabilities alongside state-of-the-art RL in real-world deployments.
This role is about fixing some of the largest problems within general-purpose robotics today.
Our software stack is built from scratch in C/C++. We do not rely on common ML frameworks. We build what we need. The work is deep, interdisciplinary, and exploratory. You will translate theory, papers, and experimental results into robust, real-time systems that can be tested in simulation and, increasingly, on real robotic platforms through partners.
What You’ll Do
Explore and evaluate RL approaches in the context of robotics and embodied control of humanoid robots as well as other form factors
Design and run experiments to understand the underlying model dynamics, limitations, and failure modes, especially while adapting to real-time changes
Prototype and test learning-based components into low-level, real-time systems
Collaborate with other research engineers to guide model development and research direction through evidence, experiments, and clear reasoning
Help us understand what to test, how to test it, and why.
What You Bring
Strong research background, preferably a PhD, or an exceptional MSc with substantial experience in robotics and state-of-art RL / VLA models
Hands-on experience applying reinforcement learning to real hardware
Solid understanding of robotics fundamentals, control systems, and physical constraints
Systems-level programming mindset, with proficiency in C/C++ or similar low-level languages
Ability to translate papers, theory, and experimental ideas into working code and experiments that others can understand, test, and build upon
Curiosity and rigor when working in poorly charted territory
Bonus Signals
Experience with low-level robotic control systems (e.g., PID), real-time systems, or hardware-in-the-loop setups
Familiarity with sim-to-real challenges and mitigation strategies
Exposure to vision-language-action models or other high-level learning systems for robotics
Background in control theory or physics
Who You Are
Research-driven and comfortable working where answers are unclear
Thoughtful, evidence-oriented, and willing to challenge assumptions, including your own
Collaborative and generous with insight, not territorial about ideas
Motivated by building fundamental capabilities rather than shallow demos