Software Engineer - Machine Learning

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

Job summary

Labelbox is hiring a SWE - Machine Learning contractor to review AI-generated ML code and develop scalable ML solutions. You’ll work remotely with an asynchronous schedule and contribute across modeling, data processing, and deployment, explaining model decisions clearly.

Ideal candidates have 3–5 years in ML, strong English communication, and deep experience with TensorFlow, PyTorch or scikit-learn. Remote, hourly engagement with rolling start dates.

Qualifications

• Fluent English with strong written communication.
• Deep familiarity with ML frameworks and model development.
• 3–5 years of ML project experience is a plus.
• Bachelor’s degree in CS or related field; advanced degrees 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 for 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 outputs.

Skills

English fluency

ML expertise

Education

Bachelor's degree in CS or related field

Master's or PhD preferred

Tools

TensorFlow

PyTorch

scikit-learn

Job description

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

SWE - Machine Learning (Contract)

Labelbox

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.