Machine Learning Engineer - Inference Maintainer & Developer Experience
$155k - $180k • Remote • NY, SF or Remote • FullTime
Posted 3mo ago
Job Location
NY, SF or Remote
Tech Stack
Remote Work Policy
Fully remote
Employment Type
FullTime
Categories
Machine Learning Engineer
About the job
Roboflow is seeking an experienced Machine Learning Engineer to maintain and improve their flagship open-source and commercial computer vision inference engine. This role is central to ensuring the quality and release cadence of inference as contribution volume, including AI-generated contributions, rapidly increases. The primary goal is to build and evolve an agentic-driven contribution and release pipeline, automating review, triage, CI/CD, and testing to safely absorb high volumes of contributions while maintaining strict quality standards. The engineer will also serve as the primary point of contact for inference, educating internal teams and customers, and streamlining the integration of new models.
Responsibilities
- Build and maintain the inference engine, ensuring its health and quality as contribution volume scales.
- Develop an agentic-driven contribution pipeline for automated review, triage, and CI/CD.
- Design and expand a test suite for nightly end-to-end runs across all inference targets.
- Define and enforce review standards for agents and contributors.
- Streamline the process of adding new models to the inference engine.
- Educate internal teams and customers on using the inference engine effectively.
- Translate new capabilities into documentation, demos, and launch materials.
- Contribute to and grow the broader open-source community around the inference project.
Requirements
- 5+ years of hands-on experience building and operating production-grade ML systems, ideally involving large-scale deployment of modern AI models.
- Strong foundation in computer vision and machine learning, understanding how models work internally and are deployed.
- Proficiency in building with AI coding agents and automating engineering processes using them.
- Experience with large-scale deployment of modern AI models.
- Ability to adapt models for real-world, high-impact use.