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 build data pipelines in Databricks for ingesting unstructured data.
• Construct retrieval-augmented generation (RAG) systems from scratch using ingested data.
• Build agentic LLM pipelines utilizing frameworks like LangChain, LangGraph, and LangSmith.
• Own orchestration of PySpark and Databricks workflows to prepare inputs and track outputs for AI models.
• Instrument evaluation metrics and telemetry to guide the evolution of prompt strategies.
• Work alongside product, frontend, and backend engineers to tightly integrate AI into user-facing flows.
• Leverage Databricks features such as Auto Loader for automatic detection of new files on cloud storage and schema changes.
• Utilize Delta Lake for reliability, security, and performance on the data lake for streaming and batch operations.
• Apply Databricks Workflows for orchestrating tasks to integrate data.
• Implement Delta Live Tables for building reliable data pipelines with a declarative approach.
  
  

• 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