Machine Learning Engineer - Inference Maintainer & Developer Experience

Role Description

Inference is growing fast — and so is the volume of contributions, increasingly authored with the help of AI agents. That's a great problem to have, but it's outpacing our ability to keep quality high and cut releases on a predictable cadence. Today we ship roughly weekly, and it's a fight.

We want to flip that equation. The goal is to build and continuously evolve an agentic-driven contribution and release pipeline — automated and semi-automated review, triage, CI/CD, and end-to-end testing — so that we can safely absorb a high volume of agent-generated PRs while staying firmly in control of quality.

The ideal end state: nightly end-to-end tests across every target (both standalone and on-platform), backed by a growing, world-grounded suite that validates the real health of every build. With that foundation, daily releases become routine, and we can say "yes" to far more contributions without ever lowering the bar — pushing back, by design, according to strictly defined review standards.

Alongside that, this person becomes the human face of inference: teaching internal teams and customers how to get more out of it, partnering with marketing to tell its story, and owning the (genuinely fun) work of bringing new models into the engine.

Qualifications

• 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 — understanding how computer vision models work internally and how to adapt them for real-world, high-impact use.
• Stellar agentic skills — fluently building with AI coding agents and automating the engineering process itself.
• Strong CS and systems background, with the ability to independently tackle complex programming, architecture, and reliability challenges.
• Hands-on experience with CI/CD, release engineering, and test infrastructure.
• Practical expertise with core ML technologies, including PyTorch, TensorFlow, ONNX, TensorRT, vLLM, etc.
• Strong proficiency in image and video processing, including OpenCV, DeepStream, Pillow, PyAV, etc.
• Excellent communication and soft skills — able to teach, write clearly, and collaborate across teams.
• Open source maintenance experience is a strong plus.

Requirements

• Build and maintain inference, our flagship open source and commercial CV inference engine.
• Build an agentic-driven contribution pipeline — automated and semi-automated review, triage, and CI/CD.
• Design and grow a world-grounded, ever-expanding test suite that validates real build health across every target.
• Define and enforce the "rules of the road" — the review standards and skills that agents and contributors must follow.
• Streamline how new models get added to inference.
• Teach and enable internal teams and customers.
• Be the bridge between core engineering and clients — translating new capabilities into docs, demos, stories, and launches.
• Contribute to and grow the broader open source community around the project.

Benefits

• $4000/yr Travel Stipend to travel anywhere anytime to work alongside other Roboflowers.
• $350/mo Productivity stipend for enhancing work environment.
• $350/mo AI Tools stipend.
• Cover up to 100% of your health insurance costs for you and your partner or family.
• $150/mo team lunch stipend.
• Remote first/flexible schedule allowing collaborative work.
• Unlimited PTO with an annual 2 week minimum.
• 12 weeks parental leave.
• Equity in the company.

Interview Process

• Before the Interview: Review of application, LinkedIn, Github, etc.
• Introduction Phase: Technical Assessment [15m].
• Team Interview Phase:
  • Live coding [45m].
  • Home assignment [30m].
  • Meet with Inference Core team member [60m].
  • Meet with hiring manager.
• Final Interview Stage:
  • Meet with Head of Operations for a culture discussion [45m].
  • Meet with CEO [30m].