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 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.
