Computer Vision AI & ML Engineer

Responsibilities

• Develop and optimize deep learning models for object detection, segmentation, tracking, and 3D scene understanding using multi-modal sensor data. - Build scalable pipelines for data processing, training, evaluation, and deployment into real-world and real-time systems. - Design labeling strategies and tooling for automated annotation, QA workflows, dataset management, augmentation, and versioning. - Implement monitoring and reliability frameworks, including uncertainty estimation, failure detection, and automated performance reporting. - Conduct proof-of-concept experiments to evaluate new algorithms and perception techniques; translate research insights into practical prototypes. - Collaborate with robotics, systems, and simulation teams to integrate perception models into production pipelines and improve end-to-end performance. ## Preferred Qualifications

• Strong experience with deep learning frameworks (PyTorch, TensorFlow, or JAX). - Background in computer vision tasks such as detection, segmentation, tracking, or 3D scene understanding. - Proficiency in Python; familiarity with C++ is a plus. - Experience building training pipelines, evaluation frameworks, and ML deployment workflows. - Knowledge of 3D geometry, sensor processing, or multi-sensor fusion (RGB-D, LiDAR, stereo). - Experience with data annotation tools, dataset management, and augmentation techniques. - Familiarity with robotics, simulation environments (Isaac Sim, Gazebo, Blender), or real-time systems. - Understanding of uncertainty modeling, reliability engineering, or ML monitoring/MLOps practices.