Machine Learning Engineer - Inference Maintainer & Developer Experience

Roboflow is a company focused on making the world programmable through artificial intelligence and computer vision tools. They are seeking a Machine Learning Engineer to maintain and develop their inference engine, ensuring high-quality contributions and streamlining the integration of new models.

Responsibilities:

• Build and maintain inference, our flagship open source and commercial CV inference engine, keeping it healthy and high-quality as contribution volume scales
• Build an agentic-driven contribution pipeline — automated and semi-automated review, triage, and CI/CD — so we can safely accept a high volume of agent-generated PRs and move from weekly releases toward daily ones
• Design and grow a world-grounded, ever-expanding test suite that validates real build health across every target (standalone and on-platform), with the goal of nightly end-to-end runs across all of them
• Define and enforce the "rules of the road" — the review standards and skills that agents and contributors must follow. Exercise sharp judgment on when to merge fast and when to push back, and encode that judgment into the system itself
• Streamline how new models get added to inference (the most fun part of the job) — making it dramatically faster and easier to bring the latest computer vision and ML models to our users
• Teach and enable internal teams and customers. Keep our Field Engineers and Support team a step ahead so they can self-serve and go deeper, and help customers get the full value of the product
• Be the bridge between core engineering and clients — translating new capabilities into docs, demos, stories, and launches which would help people use inference more effectively
• Contribute to and grow the broader open source community around the project

Requirements:

• 5+ years of hands-on experience building and operating production-grade ML systems, ideally involving large-scale deployment of modern AI models
• A real CV/ML foundation — you understand what inference does: how computer vision models work internally, how they're deployed across diverse environments, and how to adapt them for real-world, high-impact use
• Stellar agentic skills. You build with AI coding agents fluently and have a track record of using them not just to ship features, but to automate the engineering process itself — review, triage, testing, and CI. You have strong instincts for where agents excel and where they need guardrails
• Strong CS and systems background, with the ability to independently tackle complex programming, architecture, and reliability challenges and exercise sound judgment on when to move fast and when rigor is essential
• Hands-on experience with CI/CD, release engineering, and test infrastructure — you've built or substantially improved automated testing and delivery pipelines before
• Practical expertise with core ML technologies, including several of the following: PyTorch, TensorFlow, ONNX, TensorRT, vLLM (or other LLM/model deployment tools)
• Strong proficiency in image and video processing, including several of the following: OpenCV, DeepStream, Pillow, PyAV, hardware-accelerated video decoding. Experience with video streaming protocols is an advantage
• Excellent communication and soft skills. You can teach, write clearly, and collaborate across engineering, support, field, and marketing — and you actually enjoy it. You're comfortable being a public-facing voice for a project
• Open source maintenance experience is a strong plus — you know what it takes to steward a busy repo and a community of contributors