Machine Learning Applied Scientist

You will create, solve, review, and validate machine-learning engineering tasks against correctness and performance requirements. The work combines model development, software implementation, numerical computing, and careful technical testing.

• Develop and validate machine-learning models, training pipelines, and inference systems.
• Implement model components, data pipelines, evaluation systems, and numerical methods.
• Build reproducible workflows with Python and command-line tools.
• Optimize memory use and throughput, debug numerical or system-level failures, and verify technical results.
• Use coding agents in a coding-heavy workflow.

This is a fully remote, part-time contractor opportunity with flexible scheduling. The role is listed as entry level, while the work requires advanced academic or professional preparation in a quantitative field.

• Pay: $60 to $120 per hour equivalent, paid per task that meets the project specifications.
• Time: Approximately 15 hours per week, with flexible scheduling.
• Work arrangement: Fully remote contractor role.
• Languages: Fluent in English.
• Location: Open to candidates in Algeria, Argentina, Austria, Bahrain, Bangladesh, Belgium, Brazil, Canada, Chile, Colombia, Denmark, Egypt, France, Germany, Ghana, Greece, India, Indonesia, Italy, Japan, Jordan, Kazakhstan, Kenya, Kuwait, Kyrgyzstan, Lebanon, Malaysia, Mexico, Morocco, the Netherlands, Nigeria, Oman, Pakistan, Palestinian Territories, Peru, the Philippines, Portugal, Qatar, Saudi Arabia, Singapore, South Africa, Spain, Sri Lanka, Switzerland, Taiwan, Thailand, Tunisia, the United Arab Emirates, the United States, Uzbekistan, and Vietnam.
• Education: A master's degree or PhD in computer science, machine learning, artificial intelligence, applied mathematics, statistics, engineering, or a closely related quantitative discipline.
• Experience: Strong professional or research experience in machine learning and practical proficiency with Python.
• Useful background: Familiarity with PyTorch, JAX, NumPy, SciPy, SGLang, vLLM, llama.cpp, tokenizers, or transformers.
• Preferred experience: Work in a recognized technology, AI laboratory, research, or engineering environment. Exceptional open-source or academic experience may also qualify.

You apply here in about two minutes and finish on the hiring platform. The next step is a short AI interview (technical roles include a coding challenge). Passing it is how you qualify for paid tasks, so take it soon after you are invited.

AI training work is the human side of building artificial intelligence. Specialists develop, test, and review the data, models, and systems that help AI produce more reliable results, so people with advanced technical experience can take on more demanding work.