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About the Role
ML Engineer We are looking for highly skilled Machine Learning Experts to contribute to an AI training project involving model development, training and inference systems, numerical computing, performance optimization, and Python. The work involves creating, solving, reviewing, and validating challenging machine-learning engineering tasks. A representative task may require implementing or modifying a model, constructing a reproducible training or inference workflow, optimizing memory or throughput, debugging numerical or system-level failures, and verifying that the resulting implementation satisfies objective correctness and performance requirements.
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.
You're a Good Fit If You
Required skills: Machine Learning
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.
Engaged directly through micro1. Apply via the link below - micro1 handles onboarding and payment.
Common Questions
micro1 has a reliability rating of reliable based on the listings we track, with an onboarding time of 1-3 days. See our full micro1 review for the details behind that rating.
Applying takes you to micro1's own website, where the application is completed. We are not a recruiter or employer and do not process applications directly - we may be recorded as the referring source.
$80-$150/h - Engaged directly through micro1. Apply via the link below - micro1 handles onboarding and payment.
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.
