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Machine Learning Data Engineer - Systems & Retrieval

Zyphra • San Francisco, CA

Not Applicable Posted March 14, 2026 Job link

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Requirements

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

Preferred Skills

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

Education

(Not required) – Academic or industry background in data mining, search, recommendation systems, or IR literature

Zyphra is an artificial intelligence company based in San Francisco, California.
The Role:
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.
You’ll work across:

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
Requirements:

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
Bonus Skill Set:

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
Why Work at Zyphra:

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
Benefits and Perks:

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
If you're excited by the challenge of high-scale, high-performance data engineering in the context of cutting-edge AI, you’ll thrive in this role. Apply Today!