Hayden AI

At Hayden AI, we’re pioneering real world problem solving powered by AI and machine learning. From bus lane and bus stop enforcement to digital twin modeling and more, our clients use our mobile perception system to speed up transit, make streets safer, and create a more sustainable future. Our privacy first approach ensures that our technologies comply with security and privacy regulations and protect personal information while fostering innovation.

hayden.ai

Hayden AI

hayden.ai

HQUS

Team Size185

Open Jobs8

Total Funding$53M

Latest FundraiseUnknown

TL;DR

Headquarters / Founded: San Francisco; founded 2019

Core product: Vehicle-mounted vision AI and edge spatial analytics for transit zone enforcement and digital twin modeling

Security certifications: SOC 2 Type II and ISO/IEC 27001

Recent financing: Series C led by The Rise Fund (TPG) announced July 2024

Reported employees: Approx. 185

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Company Overview

Problem Domain

Urban mobility, transit enforcement, street safety, and spatial analytics for cities.

Founded

2019

Industry

Software Development

Funding Track Record

Series A

$20M

Series A participation included Autotech Ventures, BootstrapLabs, Modern Venture Partners

Series B

$53M

Described as oversubscribed

Series C- July 10, 2024

$90M

Announced as growth equity financing

Founders

What we do

Join the Team

Senior Engineering Manager, Deep Learning

On-SiteSan Francisco, CA, US

On-Site • San Francisco, CA, US

About Us 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.

What the job involves

As an Engineering Manager, you will lead the development and refinement of a cutting-edge perception system, leveraging deep learning for real-world applications. Your expertise in computer vision, deep learning, and team leadership will drive performance improvements and seamless integration across the company.

Responsibilities

Oversee the entire perception system development life cycle, from problem definition to deployment and ongoing improvement.
Lead a team of computer vision and perception engineers to develop and refine the system in a hands-on manner.
Spearhead the development of robust computer vision algorithms for object detection, tracking, semantic segmentation, and classification.
Champion the development and training of deep learning models for complex urban scene perception and real-time analysis.

Qualifications

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Collaborate with cross-functional teams (cloud/device) for seamless integration and monitoring of perception models.

Analyze data to identify performance bottlenecks and opportunities for enhancing the perception system.

Foster automation in the improvement cycles of deep learning models used within the perception system.

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 Robotics, Machine Learning, Computer Science, Electrical Engineering, or a related field.

2+ years leading and managing teams focused on developing real-world computer vision and perception systems using deep learning on edge devices.

Proven ability to deploy these systems with:

Deep Learning Frameworks: Expertise in PyTorch or TensorFlow (one mandatory, familiarity with both a plus).

Computer Vision Libraries: OpenCV.

Deployment Optimization Tools: TensorRT.

Strong Python programming and software design with experience in Pandas.

Experience deploying DL models to run on real-world, resource-constrained, systems with a pragmatic approach towards problem-solving.

Demonstrated proficiency in data science and traditional machine learning (SVMs, Random Forests). Prior experience with automated machine learning pipelines is desirable.

Proven industry track record with experience in:

Automated data annotation for computer vision.

Training multi-task and semi-supervised deep learning models for video data.

Familiarity with designing multi-modal deep learning models incorporating temporal context and geometrical constraints is a plus.