> Markdown version of [/jobs/ext/3989929-applied-scientists-machine-learning-remote](https://www.wearedevelopers.com/jobs/ext/3989929-applied-scientists-machine-learning-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). --- # Applied Scientists / Machine Learning - 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, Open Source Technology, Performance Tuning, AI Coding Agents, Numerical Computing, Information Technology - **Published:** October 28, 2026 - **Apply:** https://www.thejobnetwork.com/job/adcc6a4e-5e10-49c2-923b-8d5851258535/applied-scientists-machine-learning-remote ## About the Role This is a highly coding-intensive role requiring strong Python skills and hands-on experience using coding agents., 1. Master's degree or PhD in Computer Science, AI, Machine Learning, Mathematics, Statistics, Engineering, or a related field. 2. Strong professional or research experience in machine learning. 3. Practical proficiency in Python and coding agents. 4. Experience with model development, inference systems, and performance optimization. Experience at established technology companies, AI labs, research organizations, or through exceptional academic and open-source work is preferred., * Immediate availability is preferred. * Selected candidates should begin their first task within 24-48 hours after onboarding. ## Description Job DescriptionJob Title: Applied Scientist - Machine Learning (MSc. or PhD), We are seeking experienced Machine Learning Experts to contribute to an AI training project focused on model development, training and inference systems, numerical computing, and performance optimization., 1. Develop and validate machine-learning models and training pipelines. 2. Build reproducible workflows using Python and command-line tools. 3. Debug technical issues and optimize memory usage and performance. 4. Evaluate and validate machine-learning implementations against defined requirements.