SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Mistral is seeking an Applied AI Engineer focused on robotics deployment to take state-of-the-art AI models and deploy them on real robots in real-world environments for enterprise customers. This is a hands-on role at the intersection of machine learning and physical systems, where you will own the end-to-end path from model to deployed autonomy.
Key responsibilities include:
- Integrating AI models with robotic hardware, sensors, and embedded systems to bring modern perception and language-driven capabilities onto physical platforms
- Improving robustness, reliability, and safety of robotic systems operating in unstructured, real-world environments
- Testing and validating algorithms in both simulation and real-world deployments
- Analyzing field data to close the loop between deployed robot behavior and training improvements
- Bringing up, tuning, and debugging the full autonomy stack (localization, mapping, navigation, perception) on real hardware
- Developing and tuning robot navigation behaviors including path planning and obstacle avoidance for dynamic, cluttered environments
- Diagnosing and fixing end-to-end failures: mapping drift, localization issues, perception edge cases, timing problems
- Collaborating with robotics engineers, ML researchers, and systems engineers to deliver complete autonomous solutions from prototype through production
- Building and maintaining deployment, monitoring, and continuous improvement tooling for deployed systems
Mistral provides full-stack AI solutions from frontier models to developer tools and applications, partnering with enterprises in finance, manufacturing, defense, healthcare, and the public sector. The team is distributed across Europe, North America, Asia, and the Middle East, with a culture emphasizing creativity, low ego, and team collaboration.
Requirements:
- Bachelor's, Master's, or PhD in Mechatronics, Robotics, Computer Science, Electrical Engineering, or Mechanical Engineering
- Fluent English with excellent communication skills
- Strong software engineering in Python and/or C++: clean, readable, high-performance code; experience improving production codebases
- Knowledge of robotics frameworks such as ROS/ROS2
- Experience with robot perception and state estimation: SLAM, localization and mapping, path planning, sensor fusion, or computer vision
- Solid mathematical fundamentals in geometry, probability, and estimation
- Familiarity with simulation tools and robotics development environments (e.g., Isaac Sim, Gazebo)
- Strong problem-solving skills and ability to work in interdisciplinary teams
- Comfort with real-world messiness: hardware quirks, edge cases, and field surprises
- Low-ego, collaborative, and eager to learn
Nice to have:
- Experience deploying ML models on embedded or edge hardware (GPU/NPU inference, quantization, real-time constraints)
- Hands-on experience with sensor modalities such as LiDAR, depth cameras, or IMUs, including calibration and time-synchronization
- Exposure to VLM/VLA models or foundation-model approaches for robot perception and control
- Experience with navigation in GPS-denied or challenging environments, or visual-inertial odometry
- Working knowledge of autonomy tooling (Nav2, SLAM Toolbox, Cartographer, RTAB-Map, AMCL)
- Experience with behavior trees, dynamic costmaps, or fleet-level navigation
- Open-source robotics or ML project contributions
- Experience with safety-critical or regulated deployment environments
- Track record of success through personal, professional, or academic projects
