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.

This is a very coding heavy role. Candidates but have experience using coding agents with python in their workflow.

• 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.

• A master’s degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Statistics, Engineering, or a closely related quantitative discipline.
• Relevant tools may include: PyTorch JAX NumPy and SciPy SGLang vLLM llama.cpp Hugging Face Transformers Hugging Face Tokenizers Equivalent tools may also be considered when the candidate demonstrates directly relevant depth. Experience at a well-established technology company, AI laboratory, research organization, or other recognized engineering environment is strongly preferred. Exceptional open-source or academic experience may also qualify.

Apply to the role and complete the screening questions.
Complete an AI interview of approximately 30 minutes.
Complete the hiring manager review.

Work arrangement: Fully remote

Eligible locations: Worldwide

• Location: Global, fully remote.

• Compensation is output-based. Experts are paid per task that meets the project specifications. The time required to complete each task may vary depending on the expert’s experience and workflow.
• Minimum submission requirements apply.