Senior Machine Learning Engineer

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.

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, Num Py/Sci Py, SGLang, vLLM, llama.cpp, tokenizers, and transformers.

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.