Founding Engineer

About the role

About us

Multiplier makes an AI platform for professional stockpickers. We tell friends and investors about the grand vision—making AI that complements humans rather than substituting for them—but we're starting narrow. Here's what we tell customers:

“You know you need AI. But you've been offered two bad options: either you cobble together siloed point solutions (AlphaSense, ASKB, LinqAlpha) or take on a new full-time job as Claude-wrangler. Meanwhile, there's more information to process and the markets move faster than ever. You need to unify fundamental data, email, trade history, risk metrics, sellside research, charting, top-of-funnel screening, firm context, models, your portfolio, and the market news you need—all in one place for the humans and AIs to self-improve. Multiplier does it for you. We launched in May and are live with six hedge funds. We're backed by Y Combinator, Lux Capital, the Chairman of Fortress, co-CIOs of Bridgewater, et cetera.”

CTO/President Ben Finch, with whom you’d most closely be working, came up with the idea while working as founding researcher and chief of staff at Sentient. CEO Ian McInnis was an investor, builder of investment systems (LLM and non-LLM alike), and general AI pontificator (“thought leader” if you’re being sycophantic) at Bridgewater Associates. COO Ryan Winkler was an investor at StepStone. Previously, he captained the soccer team at Princeton, where he, Ben, and Ian founded an investment firm.

About the role

The product itself is a single pane of glass for all human and agent investment work. (Think: an IDE, but for investors instead of programmers.) Our users are nontechnical but sharp and demanding.

Work will be extremely full-stack. Illustratively, your tasks could include:

- Deciding what infra providers to use for post-training

- Constructing model benchmarks from client Excel files

- Setting up custom model-routing

- Designing and implementing the world's best UI for non-coding agents

- Deploying self-building factor models across diverse investor types

- Writing internal tooling to speed up onboarding

- Making a prose-desloppifying gate

- Building custom skills for a crummy alt data API

- Managing auth across a dozen portals

- Debugging on-prem deployments

- Erecting pipelines for mass document processing

- Deducing client frameworks for news filtration

- Wrestling with Microsoft enterprise infra

- Improving performance

- Reading agent traces to iterate on prompts

- Architecting new features off of vague client desires

- Folding manual QA into CI/CD

In your first month, we’ll get you up to speed on the business and the codebase. You’ll familiarize yourself further by making performance and stability improvements throughout the stack. In the second month, you’ll build a new core feature. In the third month, we’ll have more data on where your strengths lie, and we can steer you more toward owning client deployments (we’ll teach you enough about investing to converse intelligently) or more toward deeply technical infra work. If clients like your product ideas, we can steer in that direction too.