Deep Learning Engineer II

Deep Learning Engineer II Responsibilities

• 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, evaluate emerging deep learning techniques applicable to perception and intelligent mobility.

• 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 fields.
• Two years of experience in roles such as Perception Engineer (Deep Learning), Graduate Researcher, Computer Vision Researcher, Software Engineer, Machine Learning Engineer, Graduate Student, Summer Intern, or comparable deep learning engineering role.
• One year of work experience with machine learning, deep learning, and computer vision, focusing on image classification, object detection, semantic segmentation, and urban scene understanding; leveraging large foundational models for various 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 for motion forecasting or trajectory prediction of dynamic agents, applied to vehicle movement classification;
  Python programming with foundations in software design principles; proficiency in PyTorch or Tensor Flow; cloud‑based deployments on 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.

$ to $ per year

Hayden AI Technologies, Inc.
460 Bryant Street, Suite 100
San Francisco, CA 94107