AI & ML Engineer
tide rockDetroit (MI)
About the role
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 themLLM-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 / MLTrue 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
Before you apply
Applying takes about a minute. These four things decide how fast it moves after that.
Your profile is current
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Two examples you can talk through
Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.
A number in mind
What you're on now and what would make you move. We negotiate better when we know both.
Your notice period
Employers plan around it, and it's the question that stalls offers most often.
Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.
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