Backend Engineer (ML Platform)

Posted 52 days ago

Job Description

This job posting has expired and no longer accepting applications.

About Us:

We are a profitable and growing company, originating in Silicon Valley and now headquartered in Taiwan. We combine intuitive design with Creative AI tech to create inspiring products for millions of people worldwide.

We offer a fun, creative, and international workplace with competitive compensation, stock options, flexible hybrid work, free lunch, and more.

Ready to make a big impact with a talented team? Come create with us.

About the Role:

At PicCollage, our Machine Learning, Algorithms & Data (MLAD) team thrives on solving complex challenges with elegance and scale. As a Backend Engineer (ML Platform), you’ll be at the heart of this—designing and building robust platforms and pipelines that make it seamless to develop, deploy, and monitor ML models used by millions worldwide.

You’ll collaborate closely with a team of like-minded experts who love sharing their deep domain knowledge, working together to bring cutting-edge AI research into real-world products. With a culture that champions continuous learning (yes, we sponsor trips to world-renowned conferences like CVPR), you’ll grow as you help deliver high-impact innovations—fast, reliable, and at scale.

You will work closely with ML engineers, backend teams, and product teams to bring cutting-edge AI research into real-world products and ensure that models are reliably integrated and operate efficiently in production.

This role is sometimes known as MLOps Engineer in other organizations, but we use Backend Engineer (ML Platform) to emphasize strong backend development and infrastructure skills.

What You'll Do:

Design, implement and maintain machine learning platform and data pipelines, ensuring seamless deployment, scaling, and monitoring of models in production environments.

Set up monitoring systems for deployed models and tracking key metrics.

Apply and share software engineering best practices within the context of machine learning.

Collaborate with ML engineers to ensure model performance is maintained in production and work with software engineers to integrate ML systems into the broader application stack.

Accelerate machine learning development, evaluation, and integration speed through automation of workflows, tools, and processes to enhance collaboration and efficiency.