About the Role
SPREEAI builds AI virtual try-on that fashion brands embed in their sites and apps. Every try-on starts with the same question: did we read the person and the garment correctly? This role owns the computer vision pipelines that answer it, from preprocessing through evaluation, and keeps them reliable in production.
What you'll do
Build and maintain preprocessing pipelines for try-on: segmentation, pose estimation, human parsing, and body mapping
Validate and standardize inputs so downstream models get clean, consistent data
Benchmark and integrate new vision models, and measure how they perform on our own evaluation datasets
Run dataset quality checks and fix the failures they surface
Work with ML scientists to move research into pipelines that run every day
Qualifications
Hands-on production experience in applied computer vision with Python and PyTorch
Experience with segmentation or pose estimation and a record of shipping pipelines, not only research
Comfort owning data quality and evaluation, not just model training
Comfort working in a fast-moving startup, in San Francisco (hybrid)
Why Join SPREEAI?
Real ownership: a small team where the pipelines you build run in production for brand partners
Hard, visible problems: accurate try-on across a wide range of people and garments
SPREEAI is a fast-growing, innovative AI company at the forefront of fashion and e-commerce, revolutionizing how consumers engage with fashion through lifelike photorealistic try-on technology and hyper-personalized shopping experiences. Our mission is to redefine the retail landscape with cutting-edge AI solutions that blend high fashion and technology. We thrive in a dynamic, fast-paced environment where creativity meets technology to drive real impact. If you are passionate about innovation and shaping the future of fashion, SPREEAI offers a platform to make your mark.
Summary
Owns computer vision pipelines for virtual try-on, from preprocessing through evaluation, including segmentation, pose estimation, and body mapping; validates inputs, benchmarks models, and runs dataset quality checks. Requires hands-on production experience in applied computer vision with Python and PyTorch, segmentation or pose estimation, and comfort owning data quality.
Responsibilities
Build and maintain preprocessing pipelines (segmentation, pose estimation, human parsing, body mapping); validate and standardize inputs; benchmark and integrate new vision models; run dataset quality checks; work with ML scientists to move research into production pipelines
Qualifications
Hands-on production experience in applied computer vision with Python and PyTorch; experience with segmentation or pose estimation and a record of shipping pipelines; comfort owning data quality and evaluation; comfort working in a fast-moving startup in San Francisco (hybrid)
Experience requirements
Hands-on production experience in applied computer vision