Founding AI Engineer (RAG / LLM)

Tradespace
Tradespace

Software Engineering, Data Science

Posted on Aug 4, 2026
Apply now

To apply, please email your resume to jobs@tradespace.io

Overview

Location: Remote‑US or San Francisco HQ

Compensation: Base $200k – $400 k + Equity

Reports to: CTO / Co‑Founder

Team: 14 total · 6 engineers

Stack: Python · PGVector · OpenAI / Claude / Gemini / Perplexity · Rails glue code

About Tradespace

Tradespace is an AI-powered intellectual property (IP) management platform on a mission to help innovators protect their groundbreaking ideas. Our AI platform is used by some of the world’s most innovative organizations to develop world-class IP faster and more efficiently. At Tradespace, you’ll be part of a fast-growing Series A startup backed by top investors, working at the intersection of cutting-edge technology and world-changing inventions – from quantum computing and nuclear fusion to life-saving cancer treatments.

Why This Role

You will be our first dedicated AI hire, owning the Agentic framework we use to discover new innovations and draft world-class patent applications, Green‑field scope, dedicated AI budget and an opportunity to work with a team of elite engineers and IP Attorneys to support some of the most innovative labs and companies in the world.

Day‑to‑Day

  • Design multi‑modal embedding stores for text, images & CAD
  • Build high‑recall, low‑latency retrieval APIs at scale
  • Orchestrate LLM‑based agents for patent drafting and analysis
  • Ship Python micro‑services and integrate with our Rails backend
  • Pair with engineers, product and patent attorneys to validate output
  • Experiment with the latest models and roll the best into production

    • What You’ll Own

  • AI Architecture & Roadmap: Model choices, storage strategy, agent workflows
  • Production RAG Services: Embeddings, indexing, retrieval, monitoring
  • Agentic Workflows: Tool‑using LLM chains, human‑in‑the‑loop hand‑offs
  • Quality & Reliability: Testing, observability, statistical debugging
  • Scaling Strategy: Compute budgeting and cost / latency trade‑offs

    • Must‑Have Qualifications

  • 4 + yrs shipping NLP/LLM systems to production (Python)
  • Hands‑on RAG expertise: embeddings, vector DB tuning, latency control
  • Experience debugging LLM outputs with statistical / eval frameworks
  • Ability to explain technical decisions to multidisciplinary stakeholders
  • Bias toward action in zero‑to‑one environments

    • Nice‑to‑Have

  • Multi‑modal embeddings for images or diagrams
  • Exposure to LangChain, AutoGen or similar orchestration tools
  • Familiarity with Rails or other Ruby ecosystems
  • Curiosity about patents and IP workflows

    • Hiring Process (≈ 2 week total)

  • 45‑min technical screen with CTO
  • Paid take‑home exercise (≤ 3 h) — real Tradespace data
  • De-brief with CTO and Product
  • Final interview loop (architecture deep‑dive + culture chat)

    • Benefits & Perks

  • Remote, hybrid or SF‑office flexibility
  • Hardware of choice and home‑office stipend
  • Comprehensive health, dental, vision
  • Unlimited PTO
  • 401(k)

    • Ready to build the AI engine behind tomorrow’s breakthroughs?

    Submit your resume at:

    To apply, please email your resume to jobs@tradespace.io

    Apply now
    See more open positions at Tradespace