Research Scientist

Who We Are

Our mission is to make the world programmable. Sight is one of the key ways we understand the world, and soon this will be true for the software we use, too.

We’re building the tools, community, and resources needed to make the world programmable with artificial intelligence. Roboflow simplifies building and using computer vision models. Today, over 1M+ developers, including those from half the Fortune 100, use Roboflow’s machine learning open source and hosted tools. That includes counting cells to accelerate cancer research, improving construction site safety, digitizing floor plans, preserving coral reef populations, guiding drone flight, and much more.

Our team is small relative to our impact, and we believe our user success is our success (not the inverse). A team member summarized: “Roboflow is a company full of giant brains and tiny egos.” We find software has a multiplier effect on all roles (not only product and engineering), so Roboflow employs developers across the company in design, sales, customer support, marketing, and beyond.

We’re supported by great customers and investors, having raised over 63 million from Google Ventures, Y Combinator, Craft Ventures, Sam Altman, Lachy Groom, amongst other leading software investors.

What You'll Do

As a Research Scientist, you will originate and drive the novel machine learning research directions that shape Roboflow's research agenda. Because of Roboflow’s huge userbase, we have a unique visibility into how people are using computer vision in the real world, including in what circumstances existing methods are unable to solve user problems.

Our Research Team’s mission is to introduce novel methods that generalize as well as possible across our userbase. This includes methods that learn well on diverse data, run efficiently on different hardware targets, and solve unique categories of problems. You will identify the most promising open problems in computer vision, develop original theoretical approaches and algorithms to address them, and publish your work at top conferences. You'll partner closely with our engineering team, who will take your research and translate it into production systems running at scale.

Who You Are

You are a pragmatic, experienced machine learning researcher who wants to be an important part of an exceptional team that focuses on using Roboflow's computer vision tools to impact and improve every industry. You have high agency and a bias toward action.

Masters / PhD in AI, machine learning, computer vision, robotics, or similar technical field of study, or equivalent industry experience

History of publication at conferences / journals (CVPR, NeurIPS, ICLR, etc.)

Deep understanding of ML theory and a track record of turning open problems into novel, well-formed research directions

Values scientific rigour and reproducibility

Passionate about computer vision and solving problems users value

Pragmatic approach to utility in practice (not just in theory)

Cares about open source and the broader research/developer ecosystem

Writes and communicates clearly — able to explain your work to the scientific community, the developer community, and non-research stakeholders inside Roboflow

Preferred but not required:

Familiarity with the practical constraints of optimizing models for edge hardware and training models on distributed infrastructure

Experience collaborating with engineering teams to move research from prototype to production

Where You'll Work

Roboflow’s team is distributed across the world. We currently have Hubs in New York City, San Francisco, and Brazil (and plan to open more as we grow density in new cities). We provide opportunities (like team onsites in different cities) and resources (like a $4000/yr travel stipend) to work in person with other team members as much as you'd like, while also supporting remote team members. You can work from one of our Hubs (we offer a relocation bonus), work from home, work at co-working spaces, etc. We want you to work where you work best!

What You'll Receive