Software Engineer - Machine Learning

Coding evaluation work

Help improve AI coding systems through practical machine learning expertise. The role combines evaluating generated code, writing solutions, and explaining the reasoning behind technical decisions.

Assignments

• Review generated code using Python, TensorFlow, PyTorch, scikit-learn, or similar tools for correctness, efficiency, scale, and clarity.
• Write solutions for modeling, processing data, and deployment problems at different levels of difficulty.
• Explain architecture choices, code behavior, and solution strategies for developer readers.
• Flag exceptional cases and ambiguity in tasks, datasets, and generated answers.

Qualifications

Applicants need fluent English, clear analytical thinking, and the ability to explain technical concepts in writing. Required technical knowledge includes ML frameworks such as TensorFlow, PyTorch, and scikit-learn, model development, preparation of data, and effective deployment.

A Computer Science or related bachelor’s degree may be completed or in progress; a master’s or PhD is preferred but optional.

Three to five years of ML projects or pipelines, including training, evaluation, MLOps, or cloud deployment, is advantageous. Experience in data labeling, reinforcement learning from human feedback, or other AI training work is an additional advantage.

Schedule and compensation

The engagement is an hourly contract on an at will basis. Work is fully remote and asynchronous, with at least 15 hours per week required; within that commitment, you organize your own schedule. Contributors based in the United States are preferred.

The published US pay range is $25 to $100 per hour. Starts are arranged on a rolling basis as projects launch and candidates are matched to active needs.

Selection and development

Selection includes an English language screening with an AI interviewer about your background, followed by a specialist interview covering machine learning algorithms, models, and training approaches. Domain assessments form part of the process.

The work has defined expectations and independent ownership of deliverables. Consistently strong contributors may take responsibility for new programs and mentor incoming subject experts, while helping improve models used by AI labs and major enterprises.