Data Scientist (AI Data & LLM Specialist) at Eclipse Labs

<p>Join the core team at Eclipse, where we’re building an AI agent-first marketplace that connects intelligence with real-world tasks, starting with data collection and labeling. 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.</p> <h2><strong>Qualifications </strong></h2> <ul> <li>Proven experience as a Data Scientist or Machine Learning Engineer with a focus on data quality and preparation.</li> <li>Strong understanding of data labeling methodologies and hands-on experience with data annotation platforms and workflows.</li> <li>Demonstrated experience preparing datasets for training and fine-tuning Large Language Models (LLMs), including knowledge of techniques like tokenization, embeddings, and NER.</li> <li>Proficiency in Python and common data science libraries (e.g., Pandas, NumPy, Scikit-learn, spaCy, Hugging Face).</li> <li>Experience using APIs/SDKs to automate data annotation and active learning loops.</li> <li>Excellent communication skills, with an ability to create clear documentation for technical and non-technical audiences.</li> </ul> <h2><strong>Responsibilities </strong></h2> <ul> <li>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.</li> <li>Optimize for LLM Consumption: Research, define, and prototype the optimal data formats, structures, and pre-processing steps required for fine-tuning and training LLMs on our datasets.</li> <li>Data Quality Analysis: Establish automated processes and metrics to analyze the quality of both raw and labeled data, providing feedback to improve our data collection and labeling workflows.</li> <li>Collaborate with Engineering: Work closely with the engineering team to guide the implementation of data processing pipelines and ensure the data infrastructure meets the needs of ML applications.</li> </ul> <h2><strong>Nice-to-Haves</strong></h2> <ul> <li>Experience with audio data processing and relevant libraries.</li> <li>Familiarity with data annotation platforms and tools.</li> <li>Knowledge of modern MLOps principles and practices.</li> <li>Experience with large language model data curation and Reinforcement Learning from Human Feedback (RLHF) pipelines.</li> </ul> <h2><strong>Join the Eclipse team!</strong></h2> <p>Eclipse is building the fastest Ethereum