About the role
SWE - Backend (Contract)
Labelbox • Remote (United States preferred)Shape the data that powers frontier AI
Quick facts
- Engagement - Hourly, at‑will contractor
- Schedule - Fully remote & asynchronous (min. 15 hrs/week)
- Pay Range (US) - \$25 – \$100 per hour
- Start Date - Rolling — staffed as projects launch
What you’ll do
- Review and evaluate AI-generated backend code (e.g., Node.js, Python, Java, or Go) for correctness, efficiency, scalability, and clarity-
- Write high-quality backend solutions to server-side and API development problems across varying difficulty levels
- Create clear, developer-friendly explanations for architectural decisions, code logic, and problem-solving strategies
- Identify and flag edge cases or ambiguities in problem statements or AI responses
You’re a great fit if
- Fluent in English, with strong written communication skills and the ability to clearly explain backend coding concepts
- Expertise in backend development: Deep familiarity with server-side languages and frameworks (e.g., Node.js, Python, Java, Go), RESTful APIs, databases, and asynchronous programming
- 3–5 years of experience with backend frameworks, tools, or domains (e.g., Express, Django, Spring Boot, microservices, cloud platforms) 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 & Fortune 500 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 backend expertise, including distributed systems, microservices, and backend frameworks.
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, Andreessen Horowitz, B Capital, Gradient Ventures, Databricks Ventures, and Kleiner Perkins, and trusted by leading research labs and enterprises worldwide.
Ready to Apply?
Click “Apply” above, and take one or more domain specific assessments.
We review candidates on a rolling basis and will contact you if your background matches an active project.
Aplyr's read
Labelbox is a cutting-edge data training platform focused on enhancing AI capabilities through efficient data annotation. Ideal for tech professionals passionate about AI and machine learning.
What's promising
- •Labelbox offers a robust platform that significantly accelerates AI model training.
- •The company is at the forefront of AI data annotation, a rapidly growing field.
- •Recent roles indicate a strong focus on diverse AI applications and research.
What to watch
- •The niche focus on data annotation may limit broader tech career opportunities.
- •Highly specialized roles might require advanced expertise in AI and machine learning.
- •Potential candidates may face intense competition due to the company's innovative reputation.
Why Labelbox
- •Labelbox uniquely integrates data annotation with AI model improvement.
- •The company emphasizes a forward-deployed engineering approach, embedding engineers directly with clients.
- •Its platform is designed to streamline complex data labeling processes efficiently.
Aplyr’s read is generated by AI from public sources. Was it useful?
About Labelbox
Labelbox is a data training platform that enables organizations to build and manage high-quality training datasets for machine learning applications. By streamlining the data labeling process, Labelbox empowers teams to accelerate their AI initiatives and improve model performance.
