HyperFi
Prompt Engineer / AI 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'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
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
Referral Referrals increase your chances of interviewing at HyperFi by 2x.
#J-18808-Ljbffr
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
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
Referral Referrals increase your chances of interviewing at HyperFi by 2x.
#J-18808-Ljbffr