ML Vision Engineer
RetailNextLast week
- Where you can work from
- Remote — Taiwan onlyTaiwan
- Salary
- Salary not disclosedThe employer did not publish a range. We do not estimate one.
- Job type
- Full-time
- Experience
- Senior
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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
Originally posted on Himalayas
Job availability & source
Still listed on the employer's own board at our last check, 7 days ago
This job was originally posted by the employer or source shown below. We re-check the original listing regularly to keep its availability current, and send you straight to it to apply — we never sit in between.
- Source
- Himalayas
- Posted by the employer
- 11 Oct 2026
- First seen by us
- 13 Oct 2026
- Last checked
- 18 Oct 2026
via Himalayas — view on source
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