Remote Part-Time ML Engineer - 15h/Week, $100-$150/hr

24-Mag Llc; New York, Northern (NY, KY); Hybrid; USD 138,000 - 207,000; Part time

Job summary

24-MAG LLC is seeking experienced Machine Learning Engineers for a specialised part-time consulting opportunity. The role focuses on model development, training and inference systems, numerical computing, performance optimisation, and Python-based ML engineering.

Selected professionals will build and validate ML workflows, troubleshoot numerical and infrastructure issues, and ensure objective correctness and performance.

Qualifications

• Master's degree or PhD in Computer Science, ML, AI, applied math, statistics, engineering, or related quantitative field.
• Strong professional or research experience in machine learning.
• Practical proficiency with Python.
• Experience with at least two ML frameworks, numerical libraries, or inference tools.
• Ability to debug ML systems and explain design choices.
• Experience building reproducible technical/workflows.

Responsibilities

• Develop and validate ML models, training pipelines, and inference systems.
• Implement model components, data pipelines, evaluation systems, and numerical methods.
• Build reproducible technical workflows using Python and CLIs.
• Work with tensor operations, architectures, tokenisation, batching, and generation.
• Verify implementations meet functional, numerical, and performance requirements.
• Review AI-generated code for correctness and engineering quality.

Skills

Python

ML frameworks

Numerical libraries

Workflow automation

Debug ML systems

Education

Master's degree

PhD

Tools

PyTorch

JAX

NumPy

SciPy

SGLang

vLLM

llama.cpp

Transformers

Tokenizers

Job description

24-MAG LLC is seeking experienced Machine Learning Engineers for a specialised part-time consulting opportunity. The role focuses on model development, training and inference systems, numerical computing, performance optimisation, and Python-based ML engineering.

Selected professionals will build and validate ML workflows, troubleshoot numerical and infrastructure issues, and ensure objective correctness and performance.