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're looking for a
Prompt Engineer / AI Engineer to join early. Someone who knows how to move from prototype to production, who can design prompts, evaluate them, and wrap them in real workflows that run reliably. You’ll work closely with the CTO and Tech Lead to build intelligent systems that plug into a larger product — not just toy demos. If you’re fluent in RAG, LangChain, and PySpark, and care about real-world agent behavior, this is your kind of role.
💥 What You’ll Do
- Build agentic LLM pipelines using LangChain, LangGraph, and LangSmith
- Design and iterate on prompt strategies, with a focus on consistency and context
- Construct retrieval-augmented generation (RAG) systems from scratch
- Own orchestration of PySpark and Databricks workflows to prepare inputs and track outputs
- Instrument evaluation metrics and telemetry to guide prompt evolution
- Work alongside product, frontend, and backend engineers to tightly integrate AI into user-facing flows
🧰 Tech Stack (So Far)
- 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
There’s enough here to move fast, but still plenty of room for your fingerprints.
💻 How We Build
- 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
✅ What We’re Looking For
- 5–7 years 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, not just chains
- 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
- Confident English skills to collaborate clearly and effectively with teammates
🔥 Bonus If You
- Have built agent-like workflows with LangGraph or similar
- 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
📍 Location & Compensation
- 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
