
Job Description
About the role
We're looking for a Machine Learning Vision Engineer to help our R&D team push the boundaries of video analytics — from multi-view geometry and 3D reconstruction to multiple object tracking and facial analysis. You'll work at the core of Aurora, our own cloud-managed stereo sensor, which combines onboard shopper detection and tracking with WiFi, Bluetooth, and HD video recording.
You'll do well here if you have a strong grip on multi-view geometry, an inventive eye for new techniques, and a genuine drive to scale computer vision into large, real-world installations. This isn't research in a vacuum — the algorithms you build run in stores around the world.
Because RetailNext builds its own hardware and software end to end, you'll get a rare shot at owning problems from theory through production deployment.
Reports to: Head of Technology
Location: Taiwan, Remote
What You'll Do
Algorithm Research & Development
- Lead the development of new computer vision algorithms for shopper detection, tracking, and analytics
- Improve the performance and efficiency of existing algorithms already running in production
- Apply multi-view geometry techniques to solve image registration, mosaicking, and 3D reconstruction problems
- Prototype new approaches quickly in Python or MATLAB before hardening them for production
- Stay close to the research space so RetailNext's techniques keep pace with — and get ahead of — the field
Multi-Sensor Systems Integration
- Build robust software that integrates multiple sensors and tracking systems, including Aurora's stereo cameras, WiFi, and Bluetooth
- Design for real-world conditions — variable lighting, store layouts, and hardware constraints — not just clean lab data
- Work across the video, WiFi, and Bluetooth analytics pipelines to keep detection and tracking accurate and consistent
Applied Engineering & Delivery
- Design, implement, validate, and release applications and capabilities in C++ and Python
- Take algorithms from prototype to production-ready code that runs reliably at scale
- Validate new techniques against real deployments across large, global installations
- Partner with the rest of the R&D team to review, test, and ship new capabilities
What We're Looking For
Required
- Master's degree in Computer Science, Computer Vision, or Machine Learning
- Strong programming skills in C/C++
- Rapid prototyping experience in Python or MATLAB
- Strong understanding of multi-view geometry
- Experience designing, implementing, and releasing computer vision applications
Valued but Not Required
- Experience with multiple object tracking or facial analysis
- Experience integrating multi-sensor systems (video, WiFi, Bluetooth)
- Background applying computer vision at production scale, not just in research
- Experience with embedded or onboard processing on custom hardware
What We Offer
- Work on novel computer vision problems most teams never get near — multi-view geometry, 3D reconstruction, and multiple object tracking at production scale
- Help shape the algorithms behind Aurora, our own cloud-managed stereo sensor, from R&D through installations running around the world
- Real ownership over new algorithm development, not just maintenance — your work directly drives what ships next
- Monthly Recharge Day
- Holiday Exchange Program
- Best Self Benefit Program
- Medical Insurance
- Work from Anywhere up to 90 days
Optimize Your Resume for This Job
Get a match score and see exactly which keywords you're missing
Job Details
- Category
- Research
- Employment Type
- Full Time
- Location
- Taiwan (Remote)
- Posted
About RetailNext
RetailNext is a global leader in retail analytics for physical stores. Our real-time analytics tools help retailers and manufacturers collect, analyze, and visualize in-store data. Our patent-pending solution uses the latest video analytics, Wi-Fi detection, on-shelf sensors, and data from point-of-sale systems and other sources to provide retailers with automatic insights about how customers interact with their stores. The RetailNext platform is highly scalable and can easily integrate with promotional calendars, staffing systems, and even weather services to analyze how internal and external factors impact customer shopping patterns. This allows retailers to identify growth opportunities, make changes, and measure success. RetailNext collects data from nearly 100,000 sensors in retail stores to measure more than one billion shopping trips per year. This data is then analyzed to generate trillions of data points annually. Headquartered in Campbell, CA, RetailNext is a globally recognized brand that operates in over 40 countries.
More Roles at RetailNext




Similar Research Roles



Found this role interesting?