Machine Learning Engineer

Full-Stack AI & ML Engineer - Build the AI Infrastructure Behind a Growing Investment Firm and our Portfolio Companies

The opportunity

We're looking for a full-stack engineer who wants to build something, not maintain someone else's system, not specialize in one slice of a larger machine. Own it end-to-end: design, build, ship, and iterate.

This role will operate in a startup environment, with an awesome team, and the resources of a successful investment firm. You get the speed, ownership, and blank-canvas energy of an early stage team, without the fundraising uncertainty. Real infrastructure, real budget, real problems worth solving and no red tape.

What you'll actually do

Our AI team builds the tools to support and accelerate the growth of our investment firm and portfolio companies.

We are still building and you will be a part of that!

Current initiatives include but are not limited to:

• Building Contextual knowledge graphs to support our portfolio companies
• Proprietary data assets and the infrastructure to collect, structure, and serve them
• LLM-powered tools that reason with real business context, not just public data

Everyone has access to the same public data these days, that's not an edge. Our edge is building systems that operate intelligently on proprietary context and data. That's exactly what you would be building.

You'll work with Claude, LLMs, RAG, and knowledge graphs daily. You will own projects from whiteboard to production.

Who we're looking for

We want engineers who are genuinely excited about where AI is headed, not just using it because it's the job, but building with it on their own time because they are passionate about AI. Show us your side projects. Show us what you tinker with.

You are generalist at heart. You'd rather learn the whole stack than master one narrow piece of it. You're the type who might start your own company someday and this role is a place to build that muscle while building something real for us.

Ideal background

• 2-5 years of experience in AI / ML
• True full-stack chops — you're comfortable owning a project from cradle to grave
• Direct experience with LLMs, RAG, and knowledge graphs (or the speed to pick it up fast)
• A portfolio of side projects, experiments, or things you've built just because you wanted to