Overview
This role centers on building and maintaining Java back-end components that support LLM training and refinement. Daily work includes running model evaluations, scoring AI responses, and writing clear rationales for those judgments. You will also contribute to Supervised Fine-Tuning (SFT) datasets and collaborate on Reinforcement Learning with Human Feedback (RLHF). The position is a fully remote contractor assignment with a required 4-hour overlap with PST and a commitment of 20, 30, or 40 hours per week.
What You'll Do11
- 1Design and write maintainable code for training and optimizing AI models.
- 2Run evaluation suites to benchmark model performance and analyze the results.
- 3Rank AI responses across different domains against defined quality criteria.
- 4Produce detailed explanations that justify each evaluation and rating.
- 5Lead dataset creation and maintenance for Supervised Fine-Tuning (SFT).
- 6Work with researchers and annotators to execute RLHF and improve reward models.
- 7Design new evaluation methods that strengthen model alignment with user needs and ethical guidelines.
- 8Craft and refine ideal responses to improve clarity, relevance, and technical accuracy.
- 9Perform peer reviews of code and documentation and give constructive feedback.
- 10Partner with cross-functional teams to improve model performance and product features.
- 11Evaluate and integrate new tools and techniques into the AI training pipeline.
Requirements8
- 1Bachelor's or Master's degree in Engineering, Computer Science, or equivalent practical experience.
- 2Strong proficiency in Java, including its syntax and standard conventions.
- 3Experience building web applications with modular, scalable architectures and a focus on code readability, security, and stability through testing.
- 4Ability to write clear, well-organized, correctly annotated code that is easy to classify and review.
- 5Familiarity with training LLM models using high-quality back-end components and modern coding best practices.
- 6Experience participating in code reviews and maintaining high code quality standards.
- 7Nice to have: prior experience in software quality assurance and test planning.
- 8Excellent spoken and written English communication skills.
Who Should Apply
The ideal candidate has strong Java experience and can show real projects where they built scalable back-end systems. They are comfortable writing detailed evaluation rationales and have some exposure to LLM training or fine-tuning workflows. This role is not a good fit for someone who wants a permanent, benefits-eligible position or who cannot maintain a fixed 4-hour overlap with PST. Rejections often come from candidates who describe Java skills without tangible examples, or who produce vague, unstructured evaluation explanations during the interview.
Location
Required Skills
Application Tip
Come to the technical interview prepared to walk through a Java back-end project you built, highlighting how you handled modularity, testing, and security. Also practice writing a concise evaluation rationale for a sample AI response, because the interview will probe how you justify quality judgments.
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