Senior Machine Learning Engineer

OpenTrain AI, Inc.; United States; Remote; USD 83,000 - 165,000; Part time

Job summary

OpenTrain AI, Inc. is seeking contributors to create, solve, review, and validate challenging ML engineering tasks used in AI training.

The work focuses on model development, training and inference systems, numerical computing, performance, reproducible workflows, and objective correctness checks. You will implement or modify model components, build training and inference workflows, develop evaluation systems, create data pipelines, optimize memory use or throughput, and diagnose numerical or

Qualifications

• Advanced degree in CS/ML/AI or related quantitative field.
• Strong professional or research experience in machine learning.
• Proven ability to build reproducible programmatic workflows and validate correctness.
• Depth in ML systems, numerical computing, performance optimization, or inference workflows.
• Fluent English and background in an established tech or research environment.

Responsibilities

• Develop and validate machine-learning models, training pipelines, inference systems, and supporting infrastructure.
• Use Python, command-line tools, and coding agents to build reproducible workflows.
• Implement model components, data pipelines, evaluation systems, and numerical methods.
• Optimize memory use and throughput in model or systems implementations.
• Debug numerical and system-level failures and verify implementation quality.

Skills

Machine learning

Python

Coding agents

Reproducible workflows

Numerical computing

Performance optimization

Inference workflows

Education

Master's degree or PhD in computer science, ML, AI, applied mathematics, statistics, engineering, or related field

Tools

PyTorch

JAX

NumPy/SciPy

SGLang

vLLM

llama.cpp

tokenizers

transformers

Job description

The Work

You will create, solve, review, and validate challenging machine-learning engineering tasks used in AI training. The work focuses on model development, training and inference systems, numerical computing, performance, reproducible workflows, and objective correctness checks.

You may implement or modify model components, build training and inference workflows, develop evaluation systems, create data pipelines, optimize memory use or throughput, and diagnose numerical or system-level failures.

• Develop and validate machine-learning models, training pipelines, inference systems, and supporting infrastructure.
• Use Python, command-line tools, and coding agents to build reproducible workflows.
• Implement model components, data pipelines, evaluation systems, and numerical methods.
• Optimize memory use and throughput in model or systems implementations.
• Debug numerical and system-level failures and verify implementation quality.

What It Pays and Takes

This is remote, flexible, part-time contractor work. The listed experience level is entry level, and the role requires substantial technical preparation and machine-learning experience.

• $60 to $120 per hour equivalent, paid per task that meets project specifications.
• About 15 hours per week on a flexible schedule.
• Contractor and part-time role.
• Open to candidates in the United Arab Emirates, Argentina, Austria, Bangladesh, Belgium, Bahrain, Brazil, Canada, Switzerland, Chile, Colombia, Germany, Denmark, Algeria, Egypt, Spain, France, the United Kingdom, Ghana, Greece, Indonesia, India, Italy, Jordan, Japan, Kenya, Kyrgyzstan, Kuwait, Kazakhstan, Lebanon, Sri Lanka, Morocco, Mexico, Malaysia, Nigeria, the Netherlands, Oman, Peru, the Philippines, Pakistan, the Palestinian Territories, Portugal, Qatar, Saudi Arabia, Singapore, Thailand, Tunisia, Taiwan, the United States, Uzbekistan, Vietnam, and South Africa.
• Fluent English is required.
• 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.
• Practical proficiency with Python and coding agents.
• Experience building reproducible programmatic workflows and validating technical correctness.
• Depth in machine-learning systems, numerical computing, performance optimization, or inference workflows.
• Helpful background includes work in an established technology company, AI laboratory, research organization, or recognized engineering environment.
• Exceptional open-source or academic work may demonstrate relevant depth.
• Relevant tools may include PyTorch, JAX, NumPy/SciPy, SGLang, vLLM, llama.cpp, tokenizers, and transformers.

About AI Training Work

AI training is the human work behind modern AI systems. People create, review, and test examples so models can produce more accurate and useful results, and experienced contributors are valued for their technical judgment and subject knowledge.