Remote ML Engineer - High-Impact Contract

Alignerr Corp.; United States; Remote; USD 34,000 - 138,000; Part time

Job summary

Labelbox is seeking a SWE - Machine Learning contractor to contribute to modeling, data processing, and deployment tasks for frontier AI projects. This fully remote role emphasizes asynchronous collaboration and high-quality ML code review and development.

You will work with TensorFlow, PyTorch, and scikit-learn, and you’ll help ensure correctness, efficiency, and scalability across pipelines. Rolling start, hourly engagement, and a strong emphasis on clear explanations of model decisions and

Qualifications

• English fluency with strong written communication.
• Expertise in ML frameworks TensorFlow, PyTorch, scikit-learn.
• 3–5 years of ML projects, pipelines, or tools.
• Bachelor’s degree in CS or related field; Master’s/PhD preferred.
• Bonus: Experience with data labeling, RLHF, or AI training projects.

Responsibilities

• Review and evaluate AI-generated ML code for correctness, efficiency, scalability, and clarity.
• Write high-quality ML solutions to modeling, data processing, and deployment problems.
• Create clear explanations for model architecture decisions, code logic, and problem-solving strategies.
• Identify and flag edge cases or ambiguities in problem statements, datasets, or AI-generated responses.

Skills

ML frameworks

Model development

Data preprocessing

Deployment practices

English communication

Education

Bachelor’s degree in Computer Science or related field

Master’s or PhD preferred

Tools

TensorFlow

PyTorch

scikit-learn

Job description

Labelbox is seeking a SWE - Machine Learning contractor to contribute to modeling, data processing, and deployment tasks for frontier AI projects. This fully remote role emphasizes asynchronous collaboration and high-quality ML code review and development.

You will work with TensorFlow, PyTorch, and scikit-learn, and you’ll help ensure correctness, efficiency, and scalability across pipelines. Rolling start, hourly engagement, and a strong emphasis on clear explanations of model decisions and