About the RoleWe are seeking an innovative and highly technical Machine Learning Data Engineer to build the foundational data infrastructure that powers our Large Language Models (LLMs) and autonomous AI agents. In this role, you will sit at the bleeding edge of generative AI, focusing on the end-to-end data lifecycle-from orchestrating real-time telemetry to architecting the pipelines that train and refine our models. You will be responsible for ensuring our models are fed with high-quality, diverse, and well-structured data to drive continuous performance improvements.
Responsibilities- Automated Fine-Tuning Pipelines: Design, build, and maintain highly scalable, automated data pipelines to extract, clean, and format massive datasets specifically for LLM fine-tuning (Supervised Fine-Tuning, RLHF, and DPO).
- Real-Time Agent & LLM Evaluation: Develop robust telemetry and sampling systems to ingest real-time production data. Use this data to continuously measure, evaluate, and benchmark the real-time performance, latency, and accuracy of live LLMs and AI agents.
- Synthetic Data Generation: Architect and scale synthetic data generation workflows. Leverage foundational models to create high-variance synthetic datasets to augment training data, cover edge cases, and mitigate bias where real-world data is scarce or privacy-restricted.
- Data Quality & Curation: Implement automated data quality checks, deduplication strategies, and toxicity filtering to ensure the highest standard of data integrity entering the model training pipelines.
- Infrastructure & MLOps Integration: Partner closely with ML Researchers and DevOps to integrate data pipelines with model training infrastructure, ensuring seamless continuous integration and continuous deployment (CI/CD) for model updates.
- Vector Database Management: Manage and optimize data flows into vector databases to support Retrieval-Augmented Generation (RAG) capabilities for our agentic workflows.
Requirements- Education: Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related technical field.
- Experience: 5+ years of professional experience in Data Engineering, Machine Learning Engineering, or MLOps.
- Programming Languages: Expert-level proficiency in Python and advanced SQL.
- Generative AI & LLMs: Demonstrated hands-on experience working with LLM APIs (OpenAI, Anthropic, etc.), open-source models (Hugging Face, Llama), and orchestration frameworks (LangChain, LlamaIndex, or AutoGen).
- Synthetic Data: Proven experience employing prompt engineering and programmatic techniques for high-quality synthetic data generation and validation.
- Data Orchestration: Strong experience with workflow orchestration tools.
- Cloud & Big Data: Experience with scalable cloud infrastructure (GCP) and distributed data processing frameworks.
- These are the applicable requirements, although equivalent competencies in any of the above will also be considered.
Nice to Have- Familiarity with vector search and vector databases (e.g., Pinecone, Milvus, Weaviate, Qdrant).
- Experience with LLM evaluation frameworks (e.g., RAGAS, TruLens, DeepEval) and building automated LLM benchmarking suites.
- Understanding of privacy-preserving techniques (PII redaction pipelines) for handling sensitive data in ML training.
- Experience deploying streaming data pipelines (Kafka, Flink, or Kinesis) for real-time analytics.
- Bilingual (English/Spanish)
- Experience deploying streaming data pipelines (Kafka, Flink, or Kinesis) not just for analytics, but to feed real-time contextual data into our agentic workflows and streaming RAG systems.
What We Offer- Competitive salary
- Initial stock options grant
- Annual performance bonus
- Unlimited PTO
- Paid parental leave
- Work model: currently fully remote, with a future transition to a hybrid setup
- Health, dental, and vision plans
- 401(k) with employer matching, for US-based employees
- Continuous learning opportunities
- Empowering opportunities for growth in a dynamic entrepreneurial environment
