Software Engineer - Machine Learning

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

SWE - Machine Learning (Contract)

Labelbox

• Remote (UnitedStates preferred) Shape the data that powers frontier AI

Quick facts

• Engagement - Hourly, at‑will contractor
• Schedule - Fully remote & asynchronous (min.15hrs/week)
• Pay Range (US) - $25–$100 per hour
• Start Date - Rolling — staffed as projects launch

What you’ll do

• Review and evaluate AI-generated machine learning code (e.g., Python, TensorFlow, PyTorch, scikit-learn) for correctness, efficiency, scalability, and clarity
• Write high-quality machine learning solutions to modeling, data processing, and deployment problems across varying difficulty levels
• Create clear, developer-friendly 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

You’re a great fit if

• Fluent in English, with strong written communication skills and the ability to clearly explain machine learning concepts and code
• Expertise in machine learning: Deep familiarity with ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn), model development, data preprocessing, and deployment best practices
• 3–5 years of experience with machine learning projects, pipelines, or tools (e.g., model training, evaluation, MLOps, cloud deployment) is a plus
• Bachelor’s degree (or pursuing one) in Computer Science or a related field. Master’s or PhD preferred, but not required.
• Bonus: Experience with data labeling, RLHF, or other AI training projects

About the role

• Flexible workload — work from anywhere, on your own schedule
• High impact — your craft directly improves models used by top AI labs & Fortune500 teams
• Clear ownership — know exactly what success looks like and have autonomy to deliver
• Growth potential — consistent high performers spearhead new programs and mentor incoming SMEs

Interview process

• Complete a screening with Zara, our AI interviewer in English, to learn more about your background and experience.
• Domain-specific Zara interview to assess your ML expertise, including ML algorithms, models, and training strategies.

About Labelbox

Labelbox builds the data engine that accelerates breakthrough AI. Our platform, expert services, and marketplace let teams iterate on data as nimbly as they iterate on code, enabling safer, smarter models in production. We’re backed by SoftBank, AndreessenHorowitz, BCapital, GradientVentures, DatabricksVentures, and KleinerPerkins, and trusted by leading research labs and enterprises worldwide.