Applied Computer Vision Engineer

United States·San Franciscomid

Machine Learning EngineerData

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Quick Summary

Key Responsibilities

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,

Requirements Summary

a small team where the pipelines you build run in production for brand partners Hard, visible problems: accurate try-on across a wi

Technical Tools

Machine Learning EngineerData

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.

About the Role

~1 min read

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.

Responsibilities

~1 min read

→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

Requirements

~1 min read

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)