> Markdown version of [/jobs/ext/3872835-machine-learning-engineer-remote](https://www.wearedevelopers.com/jobs/ext/3872835-machine-learning-engineer-remote). 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 - Remote - **Company:** Yo Labs Inc. - **Location:** New York, NY, United States (Remote available) - **Contract:** Contract - **Skills:** Artificial Intelligence, Command-Line Interface, Software Debugging, Memory Management, Python (Programming Language), Machine Learning, Performance Tuning, AI Coding Agents, Numerical Computing, Information Technology, Free and Open-Source Software, Data Pipelines - **Published:** October 22, 2026 - **Apply:** https://www.careerjet.com/jobad/us6f507223013d6f14b84568691d762ac3 ## About the Role We are seeking experienced Machine Learning Experts to support AI training projects involving model development, training pipelines, inference systems, numerical computing, and performance optimization. This is a coding-intensive role requiring strong Python skills and experience using coding agents., * Master's degree or PhD in Computer Science, Machine Learning, AI, Applied Mathematics, Statistics, Engineering, or a related quantitative field. * Strong professional or research experience in machine learning. * Practical proficiency in Python and coding agents. * Experience with model development, training, inference, or performance optimization. Preferred Qualifications * Experience at a technology company, AI laboratory, research organization, or established engineering team. * Strong academic research or open-source contributions., Candidates should be ready to begin promptly, with the first task expected within 24-48 hours after onboarding. ## Description * Develop and validate machine learning models and training pipelines. * Build inference systems, data pipelines, and evaluation frameworks. * Implement model components and numerical methods. * Optimize memory usage, computational efficiency, and system performance. * Debug technical issues and validate correctness through reproducible workflows. * Use Python, command-line tools, and coding agents to complete engineering tasks.