Deep Learning Engineer II

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

Do you have experience in System design for system development?, 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.

EXPERIENCE REQUIREMENT:

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.

OTHER SPECIAL REQUIREMENTS:

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.

At Hayden AI, we are on a mission to harness the power of computer vision to transform the way transit systems and other government agencies address real-world challenges.

From bus lane and bus stop enforcement to transportation optimization technologies and beyond, our innovative mobile perception system empowers our clients to accelerate transit, enhance street safety, and drive toward a sustainable future.