Machine Learning Engineer

at  Coactive AI

Coactive makes it easy to search, filter, and analyze visual content. Increasingly image and video data captures the content we watch, the products we buy, and the work we do, and already represents 80% of internet traffic. But rather than being an asset, visual content is often a tax or even a liability because it is so hard to work with and understand. Coactive solves this by bringing structure to unstructured visual data. Rather than spending months (or years) building complex infrastructure, data teams can unlock the value of their visual data in minutes to power use cases such as content understanding and moderation, search, and analytics.
Coactive was founded by experts who shaped the fields of high-performance deep learning and data-centric AI. We have the scars from building and working with the first generation of modern machine learning systems at Google, Meta, Pinterest, eBay, Lyft, and other leading organizations. Through our decades of experience, we have developed a playbook to democratize the toughest parts of machine learning systems; no PhD required.
This is an exceptional opportunity to work with one of Forbes’ top 50 AI companies, backed by key investors such as Andreesen Horowitz and Bessemer Ventures. This position is truly a chance to be part of a category-defining team that is attracting top-tier talent interested in pushing the boundaries of what’s possible in AI.
As a software engineer, you will work coactively with the best in ML and data systems, building the foundation for understanding unstructured image and video data.

• Productionize the cutting edge techniques in data-centric AI (active learning, multimodal intelligent search) and representation learning at scale using latest foundation models.
• Collaborate with a world-class team of engineers, researchers, and scientists to create algorithms built on top of machine learning models to enable efficient, scalable search and analysis on large scale unstructured data.
• Build infrastructure to enable training and serving large scale machine learning models on image and video datasets.
• Tackle challenging problems at the intersection of cloud infrastructure and machine learning.
• Build observability and monitoring infrastructure (with Open Tracing) to ensure high availability of cloud services.
• Up-skill the team by advocating to shape engineering best practices and company culture as we grow.