Staff Software Engineer
Deep Learning
Updated on 12/10/2023
Hayden AI

51-200 employees

AI-powered mobile perception transit platform
Company Overview
Hayden AI stands out for its commitment to addressing real-world issues through AI and machine learning, offering unique solutions such as bus lane enforcement and digital twin modeling that enhance transit efficiency and safety. The company's privacy-first approach demonstrates a strong commitment to security and regulatory compliance, setting it apart in an industry where data privacy is paramount. Hayden AI's focus on creating a sustainable future further positions it as a leader in the tech industry, demonstrating a forward-thinking approach that aligns with global sustainability goals.
AI & Machine Learning
Data & Analytics

Company Stage

Series B

Total Funding

$109.4M

Founded

2019

Headquarters

San Francisco, California

Growth & Insights
Headcount

6 month growth

15%

1 year growth

15%

2 year growth

15%
Locations
San Francisco, CA, USA
Experience Level
Entry
Junior
Mid
Senior
Expert
Desired Skills
Computer Vision
CUDA
Data Structures & Algorithms
Pytorch
Tensorflow
Python
CategoriesNew
AI & Machine Learning
Software Engineering
Requirements
  • BS, MS, or Ph.D. in Robotics, Machine Learning, Computer Science, Electrical Engineering, or a related field
  • 8+ years of Expertise in deploying real-world applied computer vision (including deep learning models) on edge devices
  • Strong Python programming and software design skills, knowledge of C++
  • Familiarity with standard tools and libraries, e.g. Pytorch, OpenCV, Tensorflow, MLflow
  • Proven track record - significant industry experience and/or publications at venues such as ICRA, RSS, IROS, or CVPR
  • Experience in automated data annotation
Responsibilities
  • Develop computer vision algorithms for object detection, tracking, semantic segmentation, and classification
  • Build and train deep learning models to enable complex urban scene perception and real-time analysis
  • Participate in end-to-end development: from problem statement, data aggregation, and annotation, through model design, experiments, and training, to the deployment of the optimized model on embedded platforms and iterative improvement automation
  • Automation of improvement cycles of DL models
Desired Qualifications
  • Multi-task models training
  • Semi-supervised DL models training on video data
  • Experience in design of multi-modal DL models with temporal context and geometrical constraints
  • Understanding of optimization of DL models and deployment on embedded platforms such as the Nvidia Jetson
  • Experience in CUDA programming, low-level edge model optimization using e.g. TensorRT and similar tools
  • Experience in designing automated machine learning pipelines