About the Role
As a software engineer you will take ownership of Axon's production infrastructure and computer
vision pipelines. You’ll work with the best researchers to solve challenging problems and realize
novel ideas into products. The ideal candidate for this role would be fluent and up-to-date with
Computer Vision research and obsesses about high-quality software engineering to realize research
ideas to provide the best AI products in the market.
What You'll Do:
- Building and maintaining the infrastructure and production real-time video processing pipelines
and deploying models on various hardware platforms (edge and cloud).
- Optimizing run time efficiency of AI models and classic CV algorithms for deployment.
Requirements
Qualifications (musts):
- BS/MS in Computer Science or related field.
- 5+ years of software engineering experience in an academic or industrial setting.
- Proficiency in Python.
- Proficiency in development in Linux environment.
- The ability and desire to work in the dynamic environment of an early-stage company.
- Mentored or supervised junior software engineers or has experience as a tech lead.
Qualifications (advantages):
- Understanding of deep learning concepts state of the art in computer vision research and the
mathematics of machine learning.
- Proficiency in any of the popular computational and deep learning frameworks.
- Experience with video protocols and streaming tools (Gstreamer ffmpeg)
- Experience with hardware integration related libraries and tools such as ROS serial communications.
camera calibration tools.
About the Company
Our vision
With years of experience in deep learning technology, Axon vision is a leader in utilizing the cutting-edge technology for applications in the computer vision field.
We see a world in which a system operator has the ability to automatically understand the targets, threats and obstacles that might interrupted the complex operation of heavy machinery or a drone. Systems with visual sensors equipped with Axon’s solutions will provide insightful view of the scene and will help to reduce the cognitive load of the operators.