Micro1 review · AI Training & Data Labeling
Machine Learning Engineer — MSc or PhD (micro1)
What the work involves
micro1 is recruiting machine learning engineers to create, solve, review and validate challenging ML engineering tasks for an AI training project. The work is engineering rather than research: implement or modify a model, construct a reproducible training or inference workflow, squeeze memory or throughput, debug numerical and system-level failures, then verify that the implementation meets objective correctness and performance requirements. You will also build model components, data pipelines, evaluation systems and numerical methods, and package the lot into programmatic workflows that run from Python and the command line. The role is explicitly coding-heavy, and candidates are expected to use coding agents with Python in their workflow. They weight provenance heavily, with experience at a well-established technology company, AI lab or research organization strongly preferred, though exceptional open-source or academic work can stand in.
Contractor role, fully remote, around 15 hours a week on a flexible schedule. No prior AI training work is required.
What you'll do
- 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 programmatic workflows using Python and command-line tools
- Optimize memory use and throughput, and debug numerical or system-level failures
- Verify implementations against objective correctness and performance requirements
- Review and validate ML tasks and solutions written by others
Requirements & eligibility
- A master's degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Statistics, Engineering or a closely related quantitative discipline
- Strong professional or research experience in machine learning
- Practical proficiency with Python and coding agents
- Experience building reproducible training or inference workflows
- Able to debug numerical and system-level failures
- Strongly preferred: experience at a well-established technology company, AI laboratory, research organization or comparable engineering environment
- Exceptional open-source or academic experience may also qualify
How to apply
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