What are the responsibilities and job description for the AI Researcher position at Mentor Talent Acquisition?
This role is an opportunity to join a profitable, early-stage company building LLM-powered tools that transform how private market investors conduct research. The platform is already live with 20 enterprise clients and processes tens of thousands of research calls, providing rich real-world data to learn from and improve upon.
Role Description
This is a founding NLP / ML role. The Applied AI Researcher will play a critical role in translating product and business needs into applied ML and NLP solutions, shaping both the technical direction and the company’s AI capabilities from an early stage.
Key responsibilities include:
- Translating product and business requirements into applied ML/NLP solutions and rapid prototypes
- Developing and optimizing retrieval, search, and ranking systems using semantic embeddings, behavioral signals, and structured queries
- Building and fine-tuning custom reranking models
- Fine-tuning SLMs / LLMs for high-accuracy, low-latency tasks such as classification, query rewriting, summarization, and personalization
- Applying alignment and reliability techniques (e.g. DPO, evaluation-driven iteration) to improve trust and UX
- Designing summarization and synthesis methods to convert large volumes of unstructured data into structured insights
- Contributing to product decisions, prioritization, and roadmap discussions
- Helping shape engineering culture and best practices
- Supporting future team growth and mentorship
Requirments
The ideal candidate is a product-minded Applied AI Researcher / ML Engineer who:
- Is based in NYC and open to working onsite
- Has strong experience in NLP, ML, RAG, LLMs, and generative AI pipelines
- Has built production ML systems, particularly in search, retrieval, ranking, or structured outputs
- Can clearly explain AI/ML concepts to non-technical stakeholders
- Translates ideas into testable experiments and measurable outcomes
- Has a strong bias toward action and is comfortable owning large projects end-to-end
- Enjoys collaborating with senior stakeholders and client-facing teams
- Is excited about helping build and grow an AI team from the ground up
What Great Looks Like
- Has shipped ML systems that are used by real enterprise customers
- Balances research depth with practical, business-driven outcomes
- Comfortable operating in ambiguity and early-stage environments
- Motivated by impact rather than pure research or theory
Benefits
- Competitive salary and equity
- Private healthcare
- Gym membership
- In-office cook