Sobre este puesto de Applied Scientist – State Estimation & Integration en Rai
We are looking for exceptional Master's and PhD graduates to join our team full-time as the critical bridge between algorithmic research and on-robot deployment. This role sits at the intersection of state estimation, sensor calibration, and high-performance systems engineering. You will build the computational foundations and state estimation suites for our multi-modal sensor systems (RGB cameras, LiDARs, IMUs) and ensure these algorithms run seamlessly on physical hardware.
If you have a deep passion for writing clean, performant software, solving complex state estimation problems, and deploying your solutions directly onto advanced robotic platforms, you will want to join us.
What you'll do
Play a key role in developing and integrating robust, real-time state-estimation pipelines for multimodal sensor systems.
Write robust, low-latency modern C++ code for real-time execution directly on robotic hardware, optimizing for embedded platforms like the Jetson family of devices.
Apply classical optimization (e.g., factor graphs) as well as learning algorithms to practical, real-world robotics problems.
Profile, benchmark, and eliminate performance bottlenecks in resource-constrained Linux environments.
Design and build clean, scalable software components using modern software engineering practices and ROS 2.
Work collaboratively with researchers to provide a solid foundation for building advanced robotic systems, making complex algorithms easy to use and deploy.
What you'll need
A Master's (MS) degree or a completed (or soon-to-be-completed) PhD degree in Robotics, Computer Science, Computer Vision, Computational Science & Engineering, or a related field; early-career and junior candidates with a few years of experience are highly encouraged and welcomed to apply.
Exceptional core software engineering foundations with mandatory proficiency in Modern C++ and Python, including system architecture, data structures, and memory management.
Solid understanding of state estimation and optimization techniques, with familiarity in factor graph optimization (ideally including experience with GTSAM).
Proven software engineering experience (e.g., industry internships, significant open-source contributions, or complex project work demonstrating system design capabilities).
A strong interest in building robust tools that support both cutting-edge robotics research and real-world deployment.
Bonus
While strong core software engineering and state estimation fundamentals are required, experience in any of the following areas is a massive plus:
State Estimation & Fusion: Experience with multi-sensor state estimation, visual-inertial odometry (VIO), or SLAM algorithms.
Hardware Acceleration & ML Systems: Experience with GPU programming, CUDA, parallel processing, or optimizing embedded neural network inference and hardware-level ML acceleration.
Open Source Contributions: Significant open source projects that showcase your problem-solving skills in robotics, computer vision, or high-performance computing.
Physics & Simulation: Familiarity with computational physics, rendering pipelines (e.g., Gaussian splatting), or scalable software infrastructure for GPU-based simulation.
