Applied Scientist – State Estimation & Integration
Key details
- Location
- Zürich, Switzerland
- Work style
- Onsite
- Job type
- Full-time
- Function
- Data & ML
- Focus area
- AI / ML
- Posted
- 12h ago
Skills
About the role
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
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Play a key role in developing and integrating robust, real-time state-estimation pipelines for multimodal sensor systems.
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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.
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Apply classical optimization (e.g., factor graphs) as well as learning algorithms to practical, real-world robotics problems.
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Profile, benchmark, and eliminate performance bottlenecks in resource-constrained Linux environments.
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Design and build clean, scalable software components using modern software engineering practices and ROS 2.
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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
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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.
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Exceptional core software engineering foundations with mandatory proficiency in Modern C++ and Python, including system architecture, data structures, and memory management.
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Solid understanding of state estimation and optimization techniques, with familiarity in factor graph optimization (ideally including experience with GTSAM).
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Proven software engineering experience (e.g., industry internships, significant open-source contributions, or complex project work demonstrating system design capabilities).
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A strong interest in building robust tools that support both cutting-edge robotics research and real-world deployment.
Bonus
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State Estimation & Fusion: Experience with multi-sensor state estimation, visual-inertial odometry (VIO), or SLAM algorithms.
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Hardware Acceleration & ML Systems: Experience with GPU programming, CUDA, parallel processing, or optimizing embedded neural network inference and hardware-level ML acceleration.
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Open Source Contributions: Significant open source projects that showcase your problem-solving skills in robotics, computer vision, or high-performance computing.
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Physics & Simulation: Familiarity with computational physics, rendering pipelines (e.g., Gaussian splatting), or scalable software infrastructure for GPU-based simulation.
While strong core software engineering and state estimation fundamentals are required, experience in any of the following areas is a massive plus:
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