About the job
About Us
At Roboflow, our vision is to transform the world into a programmable space. We believe that sight is a fundamental way to perceive our surroundings, and soon, this understanding will extend to the software we use.
We are dedicated to developing tools, fostering a community, and providing resources to empower individuals to harness artificial intelligence effectively. Roboflow streamlines the process of creating and utilizing computer vision models. Over 1 million developers, including teams from half of the Fortune 100, leverage Roboflow’s open-source and hosted machine learning solutions. Our innovations support various applications, from accelerating cancer research through cell counting, enhancing construction safety, to digitizing floor plans and preserving coral reefs and much more.
With over $63 million raised from influential investors such as Y Combinator, Google Ventures, and Craft Ventures, Roboflow is backed by a strong network of customers and supporters.
Our team, the Roboflowers, is passionate about creating impactful solutions alongside dedicated colleagues. We prioritize ownership, accountability, and proactive engagement—whether tackling major projects or minor enhancements. If you are curious, hands-on with cutting-edge technology (perhaps you've explored AI products like ChatGPT), and prefer demonstrating your skills over merely discussing them, you’ll thrive in our high-autonomy environment. Many team members have entrepreneurial spirits and have initiated their journeys while still in school.
Your Role
As a Machine Learning Research Engineer at Roboflow, you will redefine the landscape of machine learning research by not only establishing benchmarks but also by addressing real-world challenges. You will collaborate within a focused, high-performing team dedicated to computer vision, diverging from the industry's current fixation on language models. Your responsibilities will include exploring and implementing innovative ideas using PyTorch, contributing to open-source initiatives, and experimenting with cutting-edge advancements in segmentation, detection, classification, and few-shot learning. Your contributions will significantly impact the development of state-of-the-art models.

