About the Role

As one of the first AI/ML hires, you will have the unique opportunity to:

Work on groundbreaking projects that combine large language models (LLMs) and traditional machine learning.
Develop novel research synthesis techniques using NLP and demonstrate their business impact.
Collaborate closely with external partners and clients to drive product innovation and growth.
Contribute to product decisions, direction, and prioritization.
Help shape the engineering culture and set best practices for AI development.

The platform is already in production with over 20 enterprise clients and tens of thousands of calls, offering rich datasets to improve and build upon.

Example Projects You Might Work On

Search ranking algorithms that integrate user interaction signals (edits, copies, engagement) with semantic and structured search to improve relevance across multiple features.
An in-house prompt optimization system that uses production signals or human labels to iteratively refine prompt quality and model output.
Developing and open-sourcing a benchmark suite to evaluate LLM outputs on domain-specific tasks.
Designing an entity deduplication and resolution system leveraging internal app signals and external data sources to clean and structure transcription output.
Building a knowledge graph pipeline that transforms unstructured interview data into dynamic, evolving knowledge representations.

About You

The ideal candidate is a product-minded ML engineer who:

Is based in New York City (in-person, 5 days per week).
Has experience building LLM-powered systems from the ground up in a product-focused environment.
Is comfortable working across the stack: data gathering and logging, experimentation, prompt engineering, and deployment.
Communicates clearly and can translate complex technical ideas for non-technical stakeholders.
Has a strong bias toward action and can deliver large projects end-to-end while collaborating with senior stakeholders.
Is excited about building and mentoring an AI/ML team over time.

Preferred Experience:

NLP techniques (text classification, entity resolution, retrieval-augmented generation).
Prompt optimization and evaluation pipelines.
Knowledge graph development.
Search and ranking infrastructure.

Benefits

Competitive compensation and meaningful equity.
Private healthcare.
Gym membership.
In-office cook.
Option to spend summers working remotely by the beach.

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