Data Scientist AI Data LLM Specialist

About Us

Eclipse is building an AI agent-first marketplace that connects intelligence with real-world tasks, starting with data collection and labeling. We are a team backed by top investors including Polychain, Tribe Capital, Placeholder, and DBA.

About the Role

We are seeking a Data Scientist to establish the foundation for how our data is labeled, processed, and prepared for consumption by next-generation Large Language Models (LLMs). Your work will be critical in transforming our raw data collections into valuable, AI-ready datasets.

Responsibilities

• Develop Data Labeling Strategies: Design and document a formal data annotation strategy, including clear, scalable, and efficient guidelines for labeling our data
• Define and enforce quality metrics, including inter-annotator agreement
• Optimize datasets for LLM Consumption: Research and prototype optimal data formats, structures, and pre-processing steps for fine-tuning and training LLMs
• Establish automated processes to analyze data quality and provide feedback to improve data collection workflows
• Collaborate closely with engineering team to implement data processing pipelines

Requirements

• Proven experience as a Data Scientist or Machine Learning Engineer with focus on data quality
• Strong understanding of data labeling methodologies and annotation platforms
• Demonstrated experience preparing datasets for LLM training
• Proficiency in Python and data science libraries (Pandas, NumPy, Scikit-learn, spaCy, Hugging Face)
• Experience using APIs/SDKs to automate data annotation
• Excellent communication skills with ability to create clear technical documentation

Nice to Have

• Experience with audio data processing
• Familiarity with data annotation tools
• Knowledge of MLOps principles
• Experience with large language model data curation and RLHF pipelines

What We Offer

Join a team that believes blockchains should be fast and highly usable. You'll do high-impact work to enhance Ethereum's scalability, with opportunities for flexible, collaborative work across synchronous and asynchronous environments.