Machine Learning Engineer

Requirements Master of Science or Engineering degree or the foreign equivalent in Robotics, Machine Learning, Computer Science, Electrical Engineering, Electrical Engineering and Computer Sciences or related Two (2) years of experience in the position offered, as a Perception Engineer (Deep Learning), Graduate Researcher, Computer Vision Researcher, Software Engineer, Machine Learning Engineer, Graduate Student, Summer Intern or a related deep learning engineer role One (1) year of work experience with all of the following: machine learning, deep learning, and computer vision, with a focus on image classification, object detection, semantic segmentation, and urban scene understanding; leveraging large foundational models for a variety of computer vision tasks; Deploying models in real-world, customer-facing production environments data augmentation and data generation pipelines for training; deep learning models for multi-sensor fusion (e.g., IMU, LiDAR, Cameras) to enable robust perception and operation in GPS-challenged environments deep learning models; developing models for motion forecasting or trajectory prediction of dynamic agents, applied to areas such as vehicle movement classification Python programming with a foundation in software design principles and with relevant machine learning libraries and Frameworks; PyTorch or TensorFlow; cloud-based deployments on platforms such as AWS, GCP, or Azure; containerization and orchestration technologies, such as Docker or Kubernetes; and cloud and edge device systems for effective model deployment and integration What the job involves Lead the research, development, and deployment of state-of-the-art deep learning models for perception of urban scenes in production environments. Architect and optimize complex machine learning systems for scalability, efficiency, and robustness, utilizing cloud-native technologies. Develop and implement training techniques such as data augmentation or model distillation pipelines to improve model robustness under diverse urban and environmental conditions Explore and integrate novel multi-modal foundational AI models, including large Vision-Language Models (VLMs), and develop strategies for their effective fine-tuning and adaptation to specific domain challenges. Drive innovation in model compression, quantization, and efficient inference techniques to optimize performance for both cloud and edge device deployments Collaborate with cross-functional teams to define machine learning roadmaps, evaluate new technologies, and contribute to the overall technical strategy. Conduct research, and evaluate emerging deep learning techniques applicable to perception and intelligent mobility #J-18808-Ljbffr