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.
Job Summary:As a Staff Machine Learning Engineer, you will play a critical role in the architecture and refinement of our cutting-edge perception system. You will leverage a wide spectrum of machine learning techniques, from foundational deep learning and computer vision models to classical algorithms and multi-modal AI models, to solve complex, real-world challenges. Your broad expertise across the machine learning landscape, combined with your proven leadership, will be essential in driving performance improvements and ensuring seamless integration across the company. In this role, you will define and lead high-impact projects, identify new opportunities for innovation, and help shape the future of ML at Hayden AI.
Responsibilities:Define and lead projects, driving the entire ML system development life cycle from problem definition and data acquisition to deployment and continuous improvement in production.
Guide and mentor cross-functional teams, fostering a collaborative environment and elevating the technical expertise across the company.
Actively contribute to the development and refinement of our machine learning models in production, writing clean, efficient, and well-documented code.
Design and train deep learning models for complex urban scene perception, classification, object detection, tracking and semantic segmentation.
Collaborate with cross-functional teams (cloud/device) for seamless integration and monitoring of machine learning models.
Design and implement automated pipelines for the continuous training, evaluation, and improvement of our machine learning models.
Analyze data to identify performance bottlenecks and uncover opportunities for system enhancement.
Communicate technical findings and insights effectively to stakeholders across the company to drive performance improvements.
Utilize data visualization tools to present complex information clearly for informed decision-making.
Ph.D. or Master's in Machine Learning, Computer Science, Robotics, Electrical Engineering, or a related field.
10+ years of relevant experience in machine learning, deep learning, and computer vision, with a focus on image classification, object tracking, semantic segmentation, and urban scene understanding.
Proven ability to deploy deep learning systems in real-world, customer-facing production environments.
Advanced Python programming skills with a strong foundation in software design principles and extensive experience with relevant machine learning libraries and frameworks.
Expertise in PyTorch or TensorFlow is required; familiarity with both is a significant plus.
Deep hands-on experience with OpenCV.
Demonstrated experience with cloud-based deployments on platforms such as AWS, GCP, or Azure.
Hands-on experience with containerization and orchestration technologies, including Docker and Kubernetes.
Experience with the management and optimization of cloud-based GPU nodes for training and inference is a plus.
Experience leveraging large foundational models for a variety of computer vision tasks.
Demonstrated proficiency in data science and traditional machine learning algorithms (e.g., SVMs, Random Forests).
Prior experience with building and maintaining automated machine learning pipelines is highly desirable.
A strong understanding of both cloud and on-device systems for effective model deployment and integration.
Top Skills
What We Do
Artificial intelligence is already impacting almost every aspect of our lives. But cities still rely on outdated technologies that are too expensive to scale and unable to deliver on their promise. As a result, streets are becoming unsafe, and cities are becoming unmanageable. Hayden AI was founded on the belief that by combining mobile sensors with artificial intelligence, we can help governments bridge the innovation gap while making traffic flow less dangerous and more efficient. Led by a team of experts in machine learning, data science, transportation, and government technology, we’ve developed the world’s first autonomous traffic management platform — simultaneously serving citizens and multi-agency missions to help cities become safer and more sustainable.
