Full Stack Engineer

We're building cutting-edge LLM-powered tools that supercharge investment research for the world's most demanding deal teams. Our clients include several of the top 10 global private equity firms, Big 4 professional services firms, and leading consulting practices: organisations responsible for deploying billions of dollars annually.

We're a profitable, bootstrapped company with a growing team of ~43 people based in London and New York. We 10x'd our revenue in 2025 and are on track to grow 2-3x again this year. Junior saves clients an average of 10 hours per week, and we're expanding fast into new verticals including investment banking, hedge funds, and research firms.

You can read more about the company mission & values here.

As one of our first engineering hires, you'll have the unique opportunity to:

• Ship quickly

• Design and build new modes of working with computers

• Work on groundbreaking projects involving LLMs

• Collaborate closely with our external partners to fuel our growth

• Contribute to product decisions, direction and prioritization

• Shape our engineering culture

• Build new frontier products at Junior to ingest more types of research inputs

• Build a voice-to-voice survey tool with extremely low latency that can autonomously conduct high volume of research tasks

• Build a mobile app from scratch in 3 weeks

• Create segmented knowledge graphs built on top of the output of unstructured ⇒ structured data transformations

• Build an AI native CRM system with deep links to the knowledge graph above

We’re looking for product-minded full-stack engineers who:

• Are based in NYC and excited to work in person with the team

• Love shipping quickly and taking ownership end-to-end

• Are eager to contribute to product decisions, not just execute tickets

• Have strong communication skills and enjoy working close to customers

• Are proficient in our tech stack: Next.js, TypeScript, and PostgreSQL

• Are excited about building AI-powered products from the ground up

• Feel like they are never building fast enough

• LLM-powered products

• Retrieval, embeddings, or prompt orchestration

• Knowledge graphs or structured data extraction

• Fast-moving startup environments