AI Data Engineer

Quantifind•Palo Alto, CA

11h•$160,000 - $200,000•Hybrid

About The Position

Quantifind is seeking an experienced AI Data Engineer to join their AI Systems team. This role will focus on driving data prototyping and experimental ETL pipelines. The ideal candidate will have a modern AI tooling perspective, experience building and managing large-scale ingestion pipelines, and a data architecture mindset focused on ontologies, frameworks, documentation, and unifying value across heterogeneous sources into knowledge graphs. Familiarity with investigative products built on open-source intelligence, managing complexity across structured and unstructured pipelines, and a strong sense of data provenance, quality, and value is essential. A responsible mindset regarding legal and compliance limits on data use is also required. The role emphasizes using AI as a first-class tool, embracing agentic coding, rapid prototyping, and multi-agent workflows, with a focus on moving fast, iterating with stakeholders, and shipping functional products.

Requirements

4+ years of professional experience, including graduate or Ph.D. work if relevant
Technical education
Forward-leaning in AI coding, with traditional technical training as a foundation
Hands-on with AI coding tools and agentic workflows (Claude Code, GPT/Codex, Cursor, Windsurf), including multi-agent approaches for testing and validation
Database management experience: PostgreSQL, RDS
Core stack: Python
Experience with large data pipelines, high-performance computing, and Spark/PySpark
Solid AWS cloud management experience
Knowledge-graph and ontology frameworks (e.g., Neo4j)
Self-driven, mission-driven, curious, and constructive — a startup mindset with strong communication skills
Ability to provide references and meet in person
US Citizen

Nice To Haves

Helpful: Spark/PySpark, Scala, and other large-scale data tooling

Responsibilities

Drive data discovery, open-source intelligence (OSINT) and related data acquisition, and commercial data ingestion.
Build and manage reliable ETL pipelines with a focus on speed from discovery to value, including database indexing, performance tuning, concurrency, and load balancing.
Perform data quality assurance and independent testing.
Work across structured and unstructured data parsing, enrichment, data architecture, ontology management, knowledge graphs, and data fusion.
Manage data compliance, documentation, and data cards.