Applied AI Engineer
Mistral AI
Applied AI Engineer
Mistral AI
Open-weight LLMs and enterprise AI deployment
Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms.
We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited.
Mistral AI is seeking an Applied AI Engineer focused on robotics deployment. You will take state-of-the-art AI models and make them work on real robots, in real environments, for real customers. This is a hands-on role at the intersection of machine learning and physical systems: you will own the path from model to deployed autonomy, working alongside robotics engineers, ML researchers, and systems engineers to deliver end-to-end autonomous solutions across diverse use cases.
Integrate AI models with robotic hardware, sensors, and embedded systems, bringing modern perception and language-driven capabilities onto physical platforms.
Improve the robustness, reliability, and safety of robotic systems operating in real-world, unstructured environments.
Test and validate algorithms in simulation and in real-world deployments, moving fluidly between the two.
Analyze field data to improve model performance and system reliability, closing the loop between what happens on the robot and what happens in training.
Bring up, tune, and debug the autonomy stack — localization, mapping, navigation, and perception — on real hardware, from sensor calibration to real-time performance.
Develop and tune robot navigation behaviors — path planning, obstacle avoidance — so systems hold up in dynamic, cluttered, and partially observable environments.
Diagnose failures end-to-end: mapping drift, localization dropouts, perception edge cases, timing issues — and fix them.
Collaborate with robotics engineers, ML researchers, and systems engineers to deliver complete autonomous solutions for customer use cases, from prototype through production deployment.
Build and maintain the tooling needed for deployment, monitoring, and continuous improvement of deployed systems.
Bachelor's, Master's, or PhD in Mechatronics, Robotics, or a relevant discipline (e.g. Computer Science, Electrical or Mechanical Engineering).
Fluent in English with excellent communication skills.
Strong software engineering in Python and/or C++: clean, readable, high-performance code; comfortable working in and 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 — geometry, probability, and estimation — and the judgment to know when a hand-crafted algorithm beats a learned one.
Familiarity with simulation tools and robotics development environments (e.g. Isaac Sim, Gazebo, or similar).
Strong problem-solving skills and ability to work in interdisciplinary teams.
Comfortable with the messiness of the real world: hardware quirks, edge cases, and field surprises don't scare you.
Low-ego, collaborative, and eager to learn.
Doesn't need roadmaps. Ships.
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 other foundation-model approaches for robot perception and control.
Experience with navigation in GPS-denied or otherwise challenging environments, or with visual-inertial odometry.
Working knowledge of established autonomy tooling — e.g. Nav2, SLAM Toolbox, Cartographer, RTAB-Map, or AMCL — and knowing when to build versus reuse.
Experience with behavior trees, dynamic costmaps, or fleet-level navigation.
Contributions to open-source robotics or ML projects.
Experience with safety-critical or regulated deployment environments.
Track record of success through personal projects, professional projects, or academia.
We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks.
For the most up-to-date details on benefits available in your location, please refer to our Benefits page.
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Open-weight LLMs and enterprise AI deployment
Mistral AI builds open-weight large language models and the tools to run them, along with inference infrastructure and enterprise products that let organizations deploy AI with control over data residency, customization, and the underlying stack. Its platform provides open-model architectures and a deployment stack for inference, governance, and integration that can run on‑premises or in the cloud. This openness and modular stack differentiate it from single-vendor hyperscalers by prioritizing data locality and flexibility. The company aims to enable reliable, customizable AI deployment at scale for businesses and public-sector customers across many countries.
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