IntuiCell AB jobb

This is a career-changing opportunity to work alongside world-class engineers and scientists on a mission to reimagine what machines can do. You’ll help us build the digital nervous system of intelligent agents - not by refining existing tools, but by inventing new systems entirely. Our software stack is built from scratch. We don’t use common ML frameworks - we build what we need. This is deep, interdisciplinary, and often exploratory work. You’ll translate theory, papers, and biological hypotheses into robust, real-time systems that can be deployed on various industrial and internal use-cases. What You’ll Do Translate neuroscience ideas and research into performant, testable code Design and run experiments to test model behaviour and learning dynamics Use your reasoning, programming and hardware skills to mature and validate the AI models in relevant environments. Work hands-on with system architecture, custom tooling, and low-level logic Collaborate with peers across neuroscience, robotics, and software engineering What You Bring Passion for exploring intelligence from first principles Research mind-set that pushes the boundaries Proven ability to write robust, well-architected code across a variety of languages Proven ability to build and debug complex software Excitement to join a team tackling some of the hardest problems of our time Bonus Signals PhD in physics, mathematics, computational neuroscience, robotics or control theory Experience in real-time systems, robotics, or simulation Who You Are Mission-driven and positive. Even in the face of complexity Curious, self-motivated, and proactive about solving hard problems An empathetic collaborator who values clarity and rigor Comfortable working across disciplines, independently and in teams

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 productionize cutting-edge research and integrate it with key partners. Your work will shape how intelligent machines learn, adapt, and act in the physical world. We are looking for the right person with a solid foundation, who finds learning fun and embraces deep, challenging work. Our Strategic Engagements team focuses on transforming research into products. We work in close collaboration with our partners and integrate the Intui model with their systems. This role involves tackling integration hurdles to bring our model to our partners, ensuring it supports both research and pilots effectively. As a Strategic Engagements Software Engineer, you will bridge our core AI research with client systems. You will adapt, productionize, and stabilize advanced algorithms for robust operation on diverse hardware - from embedded systems to complex platforms. This requires: Deep systems expertise to optimize high-performance native code. A hands-on approach to client deployment and resilient interface design. A focus on flexibility, creating configurable systems under strict performance constraints. What You’ll Do Translating research into a successful product hinges on several core attributes: reliability, portability, and flexibility. Additionally, success requires efficiency, developer experience, observability, and configurability. The role requires comprehensive DevOps responsibility across the entire software development lifecycle: Development: Coding, quality assurance, and testing. Infrastructure: Building CI/CD pipelines and release management. Operations: Server maintenance, system monitoring, and site reliability. What you bring Technical Stack: Strong proficiency in C++; comfortable with Python and TypeScript for integration and tooling. Systems Fluency: Expertise in Linux environments with a focus on memory management, concurrency, and performance. Engineering Mindset: You don't just write code; you design for testability and maintainability. Growth Orientation: A strong desire to adapt alongside an evolving tech stack. Bonus Signals Proficient in establishing build infrastructure. Demonstrated experience with Continuous Integration (CI). Familiarity with container technologies. Experience with Rust. Experience with CUDA. Who You Are Systems Thinker: You translate deep technical understanding into effective software solutions. Proactive Owner: You take full accountability for projects from inception to completion. Complexity Arbiter: You have a nuanced understanding of when to embrace or avoid technical complexity. Collaborative Peer: You prioritize sharing insights and ideas over territorial ownership. Builder: Your primary motivation is creating lasting, fundamental capabilities rather than superficial demos. Please note that we are strictly seeking direct individual contributors and do not work with consultants or third-party agencies.

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