Applied Scientist, Machine Learning (MSc or PhD)

You will create, solve, review, and validate machine-learning engineering tasks. The work focuses on model development, training and inference systems, numerical computing, Python, and performance optimization.

• Develop and validate machine-learning models, training pipelines, inference systems, and supporting infrastructure.
• 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 check technical correctness.
• Review implementations against objective correctness and performance requirements.
• Use coding agents in a coding-heavy machine-learning workflow.

This is a fully remote, part-time contractor role with flexible scheduling. The structured experience level is entry level, while the role requires substantial professional or research ability in machine learning and software implementation.

• $60 to $120 per hour equivalent, paid per task that meets the project specifications.
• Approximately 15 hours per week with flexible scheduling.
• Fully remote contractor work.
• Open to candidates in: AE, AR, AT, BD, BE, BH, BR, CA, CH, CL, CO, DE, DK, DZ, EG, ES, FR, GH, GR, ID, IN, IT, JO, JP, KE, KG, KW, KZ, LB, LK, MA, MX, MY, NG, NL, OM, PE, PH, PK, PS, PT, QA, SA, SG, TH, TN, TW, US, UZ, VN, ZA.
• Professional fluency in English.
• A 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 and practical Python skills.
• Familiarity with PyTorch, JAX, NumPy/SciPy, SGLang, vLLM, llama.cpp, tokenizers, or transformers is useful.
• Experience in technology, AI laboratory, research, or engineering settings is preferred. Strong 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 AI systems, including preparing examples, reviewing model outputs, and testing software used by models. People with strong technical experience are paid for work that requires careful judgment, reliable code, and detailed evaluation.