> Markdown version of [/jobs/ext/2624355-machine-learning-engineer](https://www.wearedevelopers.com/jobs/ext/2624355-machine-learning-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer - **Company:** Job Owl - **Location:** Boulder, CO, United States - **Experience:** Expert - **Salary:** $185,000.0 - $252,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Computer Vision, Program Optimization, Nvidia CUDA, Continuous Integration, Python (Programming Language), Machine Learning, Tensorflow, Data Streaming, Video Editing, Data Processing, Google Cloud, Pytorch, Generative AI, ONNX (Open Neural Network Exchange) Format, Machine Learning Operations, TensorRT, Data Pipelines - **Published:** August 24, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=784101a0b788d5b9 ## About the Role You thrive in ambiguity and bring a researcher's rigor with a builder's bias for shipping. You're as comfortable fine-tuning a foundational model or improving a detection pipeline as you are building the infrastructure that trains, deploys, versions, and monitors them in production. From experimentation through live inference, you will own ML problems end-to-end and build the operational backbone that makes them dependable., * Five to ten years of engineering experience, spanning applied machine learning and the infrastructure that supports it. * Deep proficiency in Python for model development, data processing, and production ML code. * Hands-on experience training and deploying models in PyTorch, TensorFLow, JAX, or similar frameworks. * Demonstrated experience taking ML models from experimentation to production, including training, evaluation, deployment, and ongoing operation. * Experience building and maintaining production systems and data pipelines. * Comfortable in a small team environment: collaborative, self-directed, and highly accountable. * Strong and proactive communication; you surface technical tradeoffs, risks, and opportunities clearly. Nice to Have * Experience fine-tuning open-weight models for specific downstream tasks. * Familiarity with Google Cloud Platform (GCP) and its ecosystem of data and ML tools. * Experience building hardware-accelerated video processing and streaming pipelines using GStreamer, NVIDIA DeepStream SDK, and CUDA. * Familiarity with model optimization and high-performance inference engines, including TensorRT, ONNX Runtime, and model compression techniques like FP16/INT8 quantization. * Experience with real-time or low-latency inference and high-throughput data processing. * Background in sports technology, broadcast, or other domains with hard real-time and accuracy constraints. * Enthusiasm for sports! Bonus points if you have competitive athletic experience. * Experience at an early-stage startup where you've worn many hats and shaped technical culture. ## Description * Design, train, fine-tune, and evaluate machine learning models across various domains (spanning foundational models, generative AI, computer vision, and predictive analytics) for real-time officiating and sports applications. * Build evaluation frameworks that hold models to broadcast and officiating standards, ensuring outputs are accurate, defensible, and robust. * Adapt state-of-the-art research into practical, production-ready solutions tailored to our unique latency and accuracy constraints. ML Ops & Production Systems * Own the deployment path for models: establishing CI/CD for training and inference, as well as robust model versioning and registries. * Build and maintain data, labeling, and training pipelines with experiment tracking and reproducibility to ensure results are traceable. * Stand up monitoring for models in production-tracking performance, drift, and data-quality signals to catch regressions before they impact live broadcasts. * Optimize models to run efficiently under real-time latency budgets, partnering with platform engineers on scalable serving and integration. Operational Excellence * Design for reliability, observability, and reproducibility from day one. * Champion sound experimental and engineering practices within a collaborative, fast-moving environment. * Continuously explore and adopt new models, techniques, and AI-assisted workflows to raise both model quality and team velocity. ## Related Videos - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [Efficient deployment and inference of GPU-accelerated LLMs​](https://www.wearedevelopers.com/videos/929-efficient-deployment-and-inference-of-gpu-accelerated-llms) - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [How machine learning can help us tell fact from fiction](https://www.wearedevelopers.com/magazine/509-how-machine-learning-can-help-us-tell-fact-from-fiction)