Data Scientist (AI Data & LLM Specialist)

Delphiventures; San Francisco (CA); On-site; USD 120,000 - 150,000; Full time

Benefits offered by this job

Competitive salary

Equity

Comprehensive benefits package

Job summary

Delphiventures in San Francisco is seeking a skilled Data Scientist to establish data labeling and preparation for AI applications. The position is integral to transforming raw data into AI-ready datasets, requiring experience with data quality methodologies and familiarity with tools for data annotation. Key responsibilities include developing labeling strategies, optimizing for Large Language Models, and collaborating with engineering. The role offers a competitive salary and benefits package.

Qualifications

• Proven experience as a Data Scientist or Machine Learning Engineer with a focus on data quality and preparation.
• Strong understanding of data labeling methodologies and hands‑on experience with data annotation platforms.
• Demonstrated experience preparing datasets for training and fine‑tuning Large Language Models.

Responsibilities

• Develop Data Labeling Strategies with clear guidelines.
• Research optimal data formats and pre‑processing for LLMs.
• Establish automated processes to analyze data quality.
• Collaborate with engineering to implement data processing pipelines.

Skills

Data preparation and quality

Data labeling methodologies

Python

Data Science libraries (Pandas, NumPy, etc.)

APIs/SDKs for automation

Excellent communication skills

Job description

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.

Qualifications

• Proven experience as a Data Scientist or Machine Learning Engineer with a focus on data quality and preparation.
• 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.
• Excellent communication skills, with an ability to create clear documentation for technical and non‑technical audiences.

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.

Nice‑to‑Haves

• Experience with audio data processing and relevant libraries.
• Familiarity with data annotation platforms and tools.
• Knowledge of modern MLOps principles and practices.
• Experience with large language model data curation and Reinforcement Learning from Human Feedback (RLHF) pipelines.

Benefits

• Competitive salary + equity + benefits package.

Eclipse Laboratories is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, sex (including pregnancy, gender identity, and sexual orientation), national origin, age, disability, genetic information, veteran status, or any other status protected by applicable laws or regulations. All employment decisions are based on qualifications, merit, and business need.