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# ML Engineer - Remote

- **Company:** Yo Labs Inc.
- **Location:** New York, NY, United States (Remote available)
- **Contract:** Contract
- **Skills:** Artificial Intelligence, Command-Line Interface, Software Debugging, Python (Programming Language), Machine Learning, NumPy, Open Source Technology, Performance Tuning, SciPy, Pytorch, AI Coding Agents, Numerical Computing, Information Technology, HuggingFace, SGLang, Data Pipelines
- **Published:** November 5, 2026
- **Apply:** https://www.thejobnetwork.com/job/11b720fa-dd49-4b5b-a150-faf89a903fb8/ml-engineer-remote

## About the Role

We are looking for highly skilled Machine Learning Engineers to contribute to an AI training project focused on model development, training and inference systems, numerical computing, performance optimization, and Python-based engineering.

This is a highly coding-intensive role. Candidates must have hands-on experience using Python and coding agents as part of their development workflow., * Master's degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Statistics, Engineering, or a closely related quantitative field.

 * Strong professional or research experience in machine learning.
 * Advanced practical proficiency in Python and coding agents.
 * Experience with one or more of the following:

 * PyTorch
 * JAX
 * NumPy / SciPy
 * SGLang
 * vLLM
 * llama.cpp
 * Hugging Face Transformers
 * Hugging Face Tokenizers

 * Equivalent tools may also be considered where candidates demonstrate relevant technical depth.
 * Experience at a technology company, AI laboratory, research organization, or recognized engineering environment is preferred.
 * Strong open-source or academic experience may also qualify.

## Description

* Develop, implement, and validate machine learning models, training pipelines, and inference systems.
 * Build model components, data pipelines, evaluation frameworks, and numerical methods.
 * Create reproducible workflows using Python and command-line tools.
 * Debug numerical, performance, and system-level issues.
 * Optimize memory usage, throughput, and overall model performance.
 * Review and validate machine learning implementations against defined correctness and performance requirements.