About Junior
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 ~28 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.
Role Description
As one of our first AI/ML hires, you'll have the unique opportunity to:
- Work on groundbreaking projects involving a mix of LLMs and traditional ML
- Come up with novel research synthesis ideas using NLP techniques and prove their business value
- Collaborate closely with our external partners to fuel our growth
- Contribute to product decisions, direction and prioritization
- Shape our engineering culture
We’re live with >20 enterprise clients, and already have 10s of thousands of calls on our platform. There’s lots of data to improve on and build.
Sample set of projects
- Build a search ranking algorithm across open text, integrating signals from user interactions (e.g., edits, copies, and engagement metrics) alongside semantic and PostgreSQL-based text search to improve relevance and ranking across multiple features.
- Build your coworker: build an in-house prompt system that can use production signals or human labels to constantly and iteratively refine and improve prompts and output
- Build and open source a benchmark for evaluating LLM output on investment research specific tasks
- Build an agentic entity deduping system that uses signals from within the app and valuable external data sources / google to clean up the last mile of great STT output: entity resolution
- Build a knowledge graph custom to every research sprint that begins as highly unstructured interview data and leverages agentic infrastructure to pull in data where necessary and constantly rebalance the graph and the derived output.
About You
We're looking for product-minded ML engineers who:
- Are based in NYC
- Are opinionated about their work and eager to contribute to product decisions
- Have strong communication skills and a desire for a client-facing role
- Are able to break down AI/ML problems for a non-technical audience clearly and succinctly, and translate their suggestions into testable experiments.
- Have experience building LLM systems from the ground up in a product-focused organization, and can help drive everything from data gathering / logging → prompt optimizing
- Have a strong bias to action, highly organised and experienced working with senior stakeholders to deliver large projects end to end
- Excited to build a team