AI & Robotics Engineer

hace 4 días

Ciudad de México, Ciudad de México Marvik A tiempo completo

We are looking for a Machine Learning Engineer with experience building and deploying perception and sensor-based ML systems in robotics or autonomous platforms. The role involves working across the full lifecycle of data, models, and deployment—from raw sensor streams to production-ready perception modules.

This is a great fit for someone who has worked with LiDAR, cameras, radar, IMUs, or multimodal pipelines, and who enjoys taking ML systems from prototype to field-tested reality.

Key Responsibilities

Perception & Sensor Fusion

  • Develop ML pipelines for multimodal sensor data (LiDAR, cameras, radar, IMU, etc.).

  • Implement or support sensor fusion approaches (classical or ML-based).

  • Build models and processing steps for perception tasks such as detection, tracking, mapping, or scene understanding.

Cross-Functional Collaboration

  • Work closely with robotics engineers, software teams, and simulation teams to ensure seamless integration of ML perception modules.

  • Contribute to design discussions involving sensing hardware, data capture strategies, and operational requirements.

Model Development & Deployment

  • Train, evaluate, and optimize ML models for robotics perception under real-world constraints.

  • Deploy ML components to diverse environments (edge devices, robotics stacks, cloud backends).

  • Collaborate on performance tuning, latency improvements, and reliability enhancements.

If you have

  • Experience working with robotics or autonomous systems.

  • Hands-on work with LiDAR, cameras, radar, or IMU pipelines.

  • Strong Python and ML fundamentals, with experience in at least one major framework (PyTorch, TensorFlow).

  • Experience designing or maintaining sensor-based ML systems, including data preparation and evaluation.

  • Understanding of model deployment in real systems (edge devices, robotics stacks, embedded platforms, or cloud).

It's a plus

  • Experience with sensor fusion frameworks, classical or ML-based.

  • Familiarity with robotics middleware (ROS/ROS2), mapping, SLAM, or navigation stacks.

  • Exposure to simulation tools (Isaac Sim, Gazebo, Unity, Webots).

  • Experience improving performance of models under real-time constraints.

  • Background working with safety, reliability, or high-availability systems.

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