ChromaDB Developer — AI Code Review & Evaluation

About OpenTrain

OpenTrain is the #1 platform for finding and building careers in AI training and data labeling. We help people start and grow careers teaching AI by connecting contributors to projects where they annotate, review, and refine the examples that modern models learn from.

This role is posted on OpenTrain because our work sits at the human/AI interface: experts review AI outputs so models learn correct, efficient, and safe behaviors.

Why AI Training Work Matters

AI training (also called data labeling or human feedback work) is the human side of building AI: people annotate, evaluate, and correct model outputs so systems learn from high-quality examples.

These roles are often fully remote, flexible, and accessible — contributors directly shape how AI behaves in real use cases, from search and retrieval to conversational assistants and code generation.

The Role

We’re hiring an experienced ChromaDB developer to review and evaluate AI-generated prompts, code snippets, and responses related to vector search and retrieval. Your feedback will help refine how models use ChromaDB for embedding-based search, indexing, and similarity queries.

This is a contract, part-time role (less than 20 hours per week), paid hourly at $25 USD, and open to remote contributors worldwide.

• Employment type: Contractor, Part-time
• Time commitment: Less than 20 hours/week
• Pay: $25 USD per hour
• Location: Worldwide, fully remote

What You’ll Do

Analyze AI-generated ChromaDB code and narrative responses for technical correctness, efficiency, and adherence to best practices in vector search and retrieval.

Label and categorize outputs to indicate issues, quality levels, and recommended corrections so the AI’s future responses improve.

• Review prompts, code snippets, and explanations generated by AI for ChromaDB-related tasks.
• Identify errors, inefficiencies, and missing best practices in embedding usage, indexing, metadata filtering, and hybrid search.
• Provide structured, written feedback and suggested fixes in clear English.
• Assign labels/categories per provided guidelines to support model training and evaluation.

Requirements

You must demonstrate strong, hands-on ChromaDB and vector database experience. The project requires practical expertise to spot subtle errors and recommend meaningful improvements.

Strong English writing skills are essential because your work will be judged on clarity, structure, and technical accuracy.

• Minimum 5+ years hands-on experience working with ChromaDB and vector databases.
• Deep knowledge of embedding models, similarity search, and large-scale dataset indexing.
• Experience integrating ChromaDB with LLMs and building or optimizing vector search pipelines.
• Ability to explain complex technical concepts clearly in English and produce structured written feedback.
• Familiarity with other vector DBs (FAISS, Pinecone, Weaviate) and best practices for indexing, metadata filtering, and hybrid search is a plus.
• Prior experience reviewing AI-generated code or performing code quality evaluations preferred.

Interview & Evaluation Guidelines

You will act as an interviewer/evaluator when assessing applicants for this role. Use hands-on, practical questions and live code checks to verify skills rather than accepting high-level or theoretical answers.

The goal is to confirm that candidates can meaningfully analyze and improve AI-generated ChromaDB code and explanations.

• Verify at least 5 years of hands-on ChromaDB/vector DB experience and ask for concrete project examples.
• Present a short ChromaDB code snippet with an intentional bug and ask the candidate to identify and fix it.
• Ask how to scale indexing and retrieval for millions of records and how to tune retrieval performance.
• Test understanding of indexing techniques, metadata filtering, hybrid search, and embedding efficiency.
• Assess ability to critique AI responses: identify errors, propose fixes, and suggest missing best practices.
• Confirm availability and willingness to complete structured labeling tasks based on AI outputs.

How to Apply & Next Steps

Apply with a resume and brief notes on 2–3 ChromaDB projects you’ve worked on, describing your role and the technical challenges you solved.

Be prepared for a technical screening that includes code review, a troubleshooting exercise, and a short written evaluation to demonstrate communication clarity.

• Include concrete project examples showing ChromaDB or vector DB work and scale handled.
• If available, provide links to public code samples or technical write-ups that demonstrate your approach to vector search and indexing.
• Applicants who provide vague, purely theoretical, or non-practical answers will not be advanced.