Machine Learning Research Engineer Engineering NY, SF or Remote

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
.

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

Roboflowers love building great things with passionate teammates. We value ownership, accountability, and a bias toward action—whether it's a big initiative or a small fix. You’re naturally curious, hands-on with new tech (maybe even played with ChatGPT or AI products early on), and prefer to show your work over talking about it. Many of us have founder mindsets and thrive in Roboflow’s high-autonomy environment—some even started as side hustlers in school.

At Roboflow, we’re redefining what it means to do machine learning research—building models that don’t just set benchmarks but directly solve real-world problems. As a Machine Learning Research Engineer, you'll join a small, high-performing team focused exclusively on computer vision, in contrast to the industry’s current obsession with language models. You'll explore and implement new ideas in PyTorch, contribute to open source projects, and experiment with the latest advances in segmentation, detection, classification, and few-shot understanding.

Your work will make state-of-the-art models accessible to a massive global developer base, with direct visibility and impact. You won’t be publishing papers for the sake of academic credit; your success is measured by what you build, how it performs, and how many people it helps.

In this role, you will:

• Research and prototype novel computer vision models and training techniques

• Lead end-to-end ML research projects: identify problem areas, survey academic/industry approaches, experiment, and implement

• Help ML Engineers implement and deliver your work

• Build and iterate on vision models in PyTorch with a focus on segmentation, detection, and few-shot understanding

• Reproduce and extend results from top academic papers using Py Torch

• Build open-source and production-quality implementations for public and internal use

• Work closely with product and engineering to ensure research is reproducible and user-facing while owning the research direction and methodology

• Drive innovation in key areas like segmentation, vision-language models, and general-purpose CV models

• Push the boundaries of what’s possible in vision through rapid experimentation and creative modeling approaches

• Maintain high standards for reproducibility, experimentation, and code quality in all research outputs

• Develop models that outperform existing state-of-the-art tools used by thousands of real users in production environments

• Contribute to open source and help shape the future of Roboflow’s research direction alongside a scrappy, inventive team

• Experience: 3–4+ years of hands-on ML research experience;
  PhD preferred but not required if equivalent industry experience

• Publications: Author on peer-reviewed papers; able to demonstrate track record of meaningful research contributions

• Technical Depth: Expert-level knowledge in PyTorch, modern vision architectures (YOLO, SAM, VLMs), and training pipelines

• Vision Expertise: Deep knowledge of object detection, instance segmentation, keypoint detection, SAM, and VLMs

• Innovative Thinker: Comes up with new ideas and pushes the boundary of what’s possible in vision

• Collaborative Researcher: Balances academic rigor with practical usability; enjoys collaborating with engineers

• Independent & Impact-Oriented: You thrive in ambiguous…