Remote ML Engineer (Contract) — Python‑Driven AI & Models

YO AI Labs; New York (NY); Remote; USD 34,000 - 83,000; Part time

Job summary

YO AI Labs is seeking highly skilled Machine Learning Engineers for a fully remote contract role. You will develop models, training pipelines, and inference systems, focusing on Python-based engineering and numerical methods.

The role is highly coding-intensive with hands-on experience using Python and coding agents, and involves optimizing performance, memory usage, and system-level behavior across scalable ML tasks.

Qualifications

• Master's degree or PhD in Computer Science, ML, AI, Applied Mathematics, Statistics, Engineering or related quantitative field.
• Strong professional or research experience in machine learning.
• Advanced practical proficiency in Python and coding agents.
• Experience with PyTorch, JAX, NumPy/SciPy, and one or more ML tooling frameworks.

Responsibilities

• 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.

Skills

Python proficiency

ML background

Coding agents experience

Education

Master's degree or PhD in Computer Science/ML/AI/Applied Math/Statistics/Engineering

Tools

PyTorch

JAX

NumPy/SciPy

SGLang

vLLM

llama.cpp

Hugging Face Transformers

Hugging Face Tokenizers

Job description

YO AI Labs is seeking highly skilled Machine Learning Engineers for a fully remote contract role. You will develop models, training pipelines, and inference systems, focusing on Python-based engineering and numerical methods.

The role is highly coding-intensive with hands-on experience using Python and coding agents, and involves optimizing performance, memory usage, and system-level behavior across scalable ML tasks.