Machine Learning Data Engineer - Systems & Retrieval

As a Machine Learning Data Engineer - Systems & Retrieval, you will build and optimize the data infrastructure that fuels our machine learning systems. This includes designing high-performance pipelines for collecting, transforming, indexing, and serving massive, heterogeneous datasets from raw web-scale data to enterprise document corpora. You’ll play a central role in architecting retrieval systems for LLMs and enabling scalable training and inference with clean, accessible, and secure data. You’ll have an impact across both research and product teams by shaping the foundation upon which intelligent systems are trained, retrieved, and reasoned over.

• Design and implementation of distributed data ingestion and transformation pipelines

• Building retrieval and indexing systems that support RAG and other LLM-based methods

• Mining and organizing large unstructured datasets, both in research and production environments

• Collaborating with ML engineers, systems engineers, and DevOps to scale pipelines and observability

• Ensuring compliance and access control in data handling, with security and auditability in mind

• Strong software engineering background with fluency in Python

• Experience designing, building, and maintaining data pipelines in production environments

• Deep understanding of data structures, storage formats, and distributed data systems

• Familiarity with indexing and retrieval techniques for large-scale document corpora

• Understanding of database systems (SQL and NoSQL), their internals, and performance characteristics

• Strong attention to security, access controls, and compliance best practices (e.g., GDPR, SOC2)

• Excellent debugging, observability, and logging practices to support reliability at scale

• Strong communication skills and experience collaborating across ML, infra, and product teams

• Experience building or maintaining LLM-integrated retrieval systems (e.g, RAG pipelines)

• Academic or industry background in data mining, search, recommendation systems, or IR literature

• Experience with large-scale ETL systems and tools like Apache Beam, Spark, or similar

• Familiarity with vector databases (e.g., FAISS, Weaviate, Pinecone) and embedding-based retrieval

• Understanding of data validation and quality assurance in machine learning workflows

• Experience working on cross-functional infra and MLOps teams

• Knowledge of how data infrastructure supports training pipelines, inference serving, and feedback loops

• Comfort working across raw, unstructured data, structured databases, and model-ready formats

• Our research methodology is grounded in methodical, step-by-step approaches to ambitious goals. Both deep research and engineering excellence are equally valued

• We strongly value new and crazy ideas and are very willing to bet big on new ideas

• We move as quickly as we can; we aim to minimize the bar to impact as low as possible

• We all enjoy what we do and love discussing AI

• Comprehensive medical, dental, vision, and FSA plans

• Competitive compensation and 401(k)

• Relocation and immigration support on a case-by-case basis

• On-site meals prepared by a dedicated culinary team; Thursday Happy Hours

• In-person team in San Francisco, California, with a collaborative, high-energy environment