Embedded AI Engineer

As an Embedded AI Engineer at Robotec.ai, you'll focus on integrating Large Language Models within our robotic systems. This includes deploying Agentic AI solutions on embedded platforms and optimizing AI models to function efficiently in real-world applications. You'll also work closely with both simulated and real robots, collaborating on hardware in the loop simulations and developing LLM-based agents to enhance our robotic capabilities. Your role will be pivotal in driving forward our R&D projects and ensuring we remain at the forefront of robotics innovation.

To qualify for the Embedded AI Engineer position at Robotec.ai, you should have a robust background in robotics, machine learning applications, or experience in developing Robotics Operating System (ROS) applications. Solid programming skills in C++ and Python are essential. Additionally, familiarity with ML frameworks such as PyTorch or TensorFlow, along with a deep understanding of ROS2, will strengthen your application. Knowledge in deploying ML on the edge and experience with LLM inference engines will be advantageous.

As an Embedded AI Engineer at Robotec.ai, you'll engage with a variety of cutting-edge technologies. Your work will involve implementing large language models, optimizing AI solutions for embedded platforms, and utilizing frameworks like PyTorch, TensorFlow, and Langchain. You will also work within the ROS ecosystem, specifically utilizing packages such as nav2 and moveit2. Experience with edge ML deployment, performance optimization, and Reinforcement Learning will enhance your contributions to our projects.

The work culture at Robotec.ai is vibrant, team-oriented, and highly collaborative. As an Embedded AI Engineer, you will work alongside some of the brightest experts in robotics and simulation, engaging in open discussions and brainstorming sessions that foster innovation. We prioritize a friendly atmosphere where teamwork thrives, and our hybrid working model provides flexibility for a balanced work-life environment. Additionally, we actively encourage contributions to open-source projects, ensuring your ideas and skills can shine.

Embedded AI Engineers at Robotec.ai can look forward to a range of attractive benefits. You will enjoy a competitive salary package of up to 25,000 + VAT, a cozy office space located in Hala Koszyki, Warsaw, and access to private medical care and a sports card. Our flexible working hours allow for a healthy work-life balance, and being part of a team passionate about advancing robotics ensures that your work is both impactful and rewarding.

When answering this question, highlight your previous projects where you integrated AI models into robotic systems. Discuss the specific AI technologies you used, such as Large Language Models, and explain how you overcame challenges during that integration. Emphasize your collaborative efforts with other team members and any measurable outcomes you achieved.

Make sure to mention your proficiency in C++ and Python, demonstrating how you've applied these languages in your projects. Discuss specific tasks, such as optimizing AI models or developing algorithms for robotic systems, showing how your programming skills made a positive impact on the outcomes of your projects.

Discuss your experience with the Robot Operating System (ROS), including any packages you've utilized, such as nav2 or moveit2. Provide examples of projects where you've leveraged ROS to solve real-world problems or improve robotic performance, underscoring your depth of knowledge in this area.

Detail the strategies you've implemented for optimizing AI models, such as quantization, pruning, or using efficient architectures. Provide examples of projects where these optimizations led to enhanced performance or reduced footprint on embedded platforms, demonstrating your understanding of the constraints and requirements of embedded systems.

Discuss the various machine learning frameworks you have experience with, such as TensorFlow and PyTorch. Highlight specific projects where you applied these frameworks, the objectives you aimed to achieve, and any significant challenges you faced while implementing machine learning solutions in robotics.

Provide insights on your experience deploying AI solutions on edge devices, focusing on practical examples of projects you've worked on. Discuss challenges you encountered regarding latency, performance, and resource management, along with how you addressed these challenges in your deployments.

Share details about a specific project that involved reinforcement learning, outlining its objectives, results, and any challenges faced. Focus on your role in the project, the algorithms implemented, and the impact of reinforcement learning on achieving successful outcomes.

Explain your systematic approach to debugging and troubleshooting, including the tools and methodologies you employ. Share a specific example that illustrates your problem-solving skills and how your interventions led to restoring functionality or improving performance in a robotic system.

Provide examples of innovations or improvements you've made in previous projects—these could include developing more efficient algorithms, enhancing performance, or implementing new technologies that significantly benefitted the overall project. Describe how your contributions aligned with team goals and project objectives.

When answering this question, express genuine enthusiasm for Robotec.ai's mission and projects. Talk about your alignment with the company's focus on open-source and human-centered robotics. Share your aspiration to contribute to cutting-edge developments in the field and how this position aligns with your career goals.