AI Systems & Data Engineer

About HyperFi

We're building the kind of platform we always wanted to use: fast, flexible, and built for making sense of real-world complexity. Behind the scenes is a robust, event-driven architecture that connects systems, abstracts messy workflows, and leaves room for smart automation. The surface is clean and simple. The interactions are seamless and intuitive. The machinery underneath is anything but. That’s where you come in.

We’re a well-networked founding team with strong execution roots and a clear roadmap. We’re backed, focused, and delivering fast.

We are seeking an AI Systems & Data Engineer to join our team. We are building a fast, flexible, and complex platform with a robust, event-driven architecture. This role requires expertise in building data pipelines within the Databricks environment, specifically for ingesting unstructured data, and leveraging that data to build AI agents.

💥 What You’ll Do

• Design and operate Databricks pipelines in Python to ingest and normalize large-scale unstructured data
• Build streaming and batch ingestion using Auto Loader, Delta Live Tables, and Workflows
• Model and maintain AI-ready lakehouse tables with Delta Lake and Unity Catalog
• Prepare retrieval and context datasets for RAG and agent systems
• Orchestrate Temporal-based workflows to coordinate data prep, validation, and AI handoff
• Enforce data quality, lineage, and access controls across pipelines
• Optimize PySpark jobs for performance, reliability, and cost
• Integrate pipeline outputs into production AI systems and APIs
• Monitor freshness, schema drift, and pipeline health
  
  

• Python (primary language for all LLM + orchestration work)
• LangChain + LangGraph + LangSmith
• Databricks + PySpark for processing, labeling, and training context
• Gemini + model routing logic
• Postgres, and custom orchestration via MCP
• GitHub Actions, GCP
  
  

• Engineers come first: your time, focus, and judgment are respected
• Deep work > chaos: fixed cycles & cooldowns protect focus and keep context switching low
• Autonomy is the default: trusted builders who own outcomes, no babysitters
• Ship daily, safely: merge early, integrate vertically, ship often, use feature flags, and keep momentum
• Outcomes over optics: solve real problems, not ticket soup
• Voice matters: from week one, contribute, improve something, and shape how we build
• Senior peers, no ego: collaborate in a high-trust, async-friendly environment
• Bold problems, cool tech: work on complex challenges that actually move the needle
• Fun is part of it: we move fast, but we also celebrate wins and laugh together
  
  

• 5-7 years of experience building production-grade ML, data, or AI systems.
• Strong grasp of prompt engineering, context construction, and retrieval design.
• Comfortable working in LangChain and building agents.
• Experience with PySpark and Databricks to handle real-world data scale.
• Ability to write testable, maintainable Python with clear structure.
• Understanding of model evaluation, observability, and feedback loops.
• Excited to push from prototype → production → iteration.
• Familiarity with Databricks Data Intelligence Platform which unifies data warehousing and AI use cases on a single platform.
• Knowledge of Unity Catalog for open and unified governance of data, analytics, and AI on the lakehouse.
• Understanding of data security concerns related to AI and how to mitigate them using the Databricks AI Security Framework (DASF).
• Confident English skills to collaborate clearly and effectively with teammates
  
  

• Have built scalable agent-like workflows on the Databricks platform.
• Have worked on semantic chunking, vector search, or hybrid retrieval strategies.
• Can walk us through a real-world prompt failure and how you fixed it.
• Have contributed to OSS tools or internal AI platforms.
• Think of yourself as both an engineer and a systems designer.
• Are familiar with the concept of a data lakehouse architecture.
  
  

• Must be based in San Francisco, Las Vegas, or Tel Aviv
• Full-time role with competitive comp
• Flexible hours, async-friendly culture, engineering-led environment