Eclipse is building an AI agent-first marketplace that connects intelligence with real-world tasks, starting with data collection and labeling. The company is seeking a Data Scientist to establish the foundation for how their data is labeled, processed, and prepared for consumption by next-generation Large Language Models (LLMs), transforming raw data collections into valuable, AI-ready datasets.
Requirements
- Strong understanding of data labeling methodologies and hands-on experience with data annotation platforms and workflows.
- Demonstrated experience preparing datasets for training and fine-tuning Large Language Models (LLMs), including knowledge of techniques like tokenization, embeddings, and NER.
- Proficiency in Python and common data science libraries (e.g., Pandas, NumPy, Scikit-learn, spaCy, Hugging Face).
- Experience using APIs/SDKs to automate data annotation and active learning loops.
- Experience with audio data processing and relevant libraries.
- Familiarity with data annotation platforms and tools.
- Knowledge of modern MLOps principles and practices.
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 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.
- 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.
- 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.
Other
- Excellent communication skills, with an ability to create clear documentation for technical and non-technical audiences.
- Flexibility. We collaborate synchronously and asynchronously, across weekly all-hands meetings, Slack messaging, and quarterly in-person meetups
- Culture. As an early member of our team, you’ll have a unique opportunity to help shape our culture. We value intellectual honesty, bias towards action, and believe every member plays a key role in achieving our ambitious goals
- Compensation. You’ll receive a competitive salary + equity + benefits package.
- Eclipse Laboratories is an equal opportunity employer.
