Lead Applied AI Engineer
The posting does not state a salary. This range is our estimate from the role, the location and the stack — treat it as a guide, not an offer.
- Level
- Senior
- Apply from
- United States
About the role
- Lead AI/ML projects for investment research.
- Develop search ranking algorithms using user signals.
- Collaborate with external partners for growth.
- Shape engineering culture and product direction.
- Build systems for LLM and NLP applications.
Skills the posting asks for
- AI
- ML
- NLP
- PostgreSQL
Summarised from the employer’s posting, which is reproduced in full below.
The employer’s full posting
Junior is building the AI operating system for investment research. Our software helps private equity firms, consultants and financial institutions complete high-stakes research workflows dramatically faster - from diligence and memo writing to market mapping, research synthesis and strategic analysis.
We’re trusted by several of the world’s top private equity firms, Big Four consultancies and leading financial institutions.
Bootstrapped and profitable
$25M+ in revenue after growing 10x last year
60+ people across the company
20-person engineering team, doubling to 40 this year
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
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.
We're looking for product-minded ML engineers who:
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
Private health insurance
Equinox gym membership
Dinner provided when working late
Team lunch provided every day at the office
Frequent international off-sites, recently including Crete and Cancún
If you’re excited to drive innovation at Junior, we’d love to hear from you!