Machine Learning Engineer – Inference Maintainer & Developer Experience

Required 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 — 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.
• Level-up your performance with AI agents.
• 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.
• Level-up your performance with AI agents.