Machine Learning Engineer – Inference Maintainer, Developer Experience

Jobtailor; California (MO); On-site; USD 150,000 - 190,000; Full time

Job summary

Jobtailor is seeking an experienced ML engineer to lead production‑grade CV inference systems. You will build and maintain the inference engine, automate reviews and CI/CD, expand test suites, and bridge engineering with clients and docs. Strong CS, ML expertise, and open‑source fluency are required.

Responsibilities include scaling deployments, improving pipelines, and mentoring internal teams; excellent communication and collaboration across engineering, support, and marketing are essential.

Qualifications

• 5+ years of hands‑on experience building and operating production‑grade ML systems.
• Understanding of inference and deployment of CV/ML models in diverse environments.
• Experience using AI coding agents for automation of review, triage, testing, and CI.
• Strong CS and systems background with ability to solve complex engineering challenges.
• Experience building or improving automated testing and delivery pipelines.
• Hands‑on with core ML tech: PyTorch, TensorFlow, ONNX, TensorRT, vLLM.
• Proficiency in image/video processing: OpenCV, DeepStream, Pillow, PyAV; knowledge of video streaming protocols is a plus.
• Excellent communication and ability to collaborate across teams and serve as public-facing voice.
• Open source maintenance experience is a strong plus.

Responsibilities

• Build and maintain inference engine and related open source and commercial components.
• Develop an agentic‑driven contribution pipeline and CI/CD to handle high PR volumes.
• Design and grow a comprehensive test suite for nightly end‑to‑end runs across targets.
• Define and enforce review standards and automation rules.
• Streamline model integration for faster deployment of latest CV/ML models.
• Educate internal teams and customers to maximize product value.
• Bridge core engineering and clients with docs, demos, and launches.
• Contribute to and grow the open source community.

Skills

Production ML systems

CV/ML foundation

Agentic AI skills

CS & systems

CI/CD & test infra

PyTorch

TensorFlow

ONNX

TensorRT

vLLM

OpenCV

DeepStream

Pillow

PyAV

Video streaming protocols

Communication & collaboration

Open source maintenance

Tools

CI/CD tools

Job description

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
• 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