Position Overview

We are seeking a Computer Vision AI & ML Engineer to design, build, and deploy advanced perception systems for real-world robotics and automation. You will work across the full machine learning lifecycle—model development, data strategy, evaluation, and production integration—to deliver robust, high-performance vision capabilities. This role combines applied research with hands-on engineering and offers the opportunity to influence both architecture and roadmap decisions.

Responsibilities

• Develop and optimize deep learning models for depth estimation, 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, depth estimation, 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.