We’re looking for a sharp, ambitious machine learning engineer fluent in building AI-native products — someone who knows how to turn messy real-world data into performant models, fine-tune and deploy LLMs, and design feedback loops that make AI systems learn continuously.

At Pincites, you’ll help transform our negotiation data into fine-tuned models that power the next generation of AI contract review. You’ll lead the evolution of our core intelligence layer — from prompt-based heuristics to data-driven models — and help define how legal negotiation knowledge becomes scalable, repeatable, and self-improving.

About Pincites

Pincites is building an AI-native contract negotiation platform for legal and procurement teams. Our product lives inside Microsoft Word and helps teams negotiate faster and more consistently. It learns how top companies — like Ramp and Vercel — negotiate today, then automates that workflow with AI-generated redlines and comments tailored to their playbook.

Backed by Nat Friedman, General Catalyst, Liquid 2 Ventures, and Y Combinator, we’ve built strong traction with enterprise legal teams globally and are on track toward building a billion-dollar company. We’re seed-stage, fully remote, and assembling a world-class team.

About the Role

You’ll design and build the systems that make Pincites truly intelligent:

Convert our 32K+ playbook “checks” into structured training datasets
Fine-tune LLMs for clause classification, redline generation, and comment writing
Build pipelines to capture feedback from human reviewers and feed it back into models
Collaborate with product and backend engineers to deploy models behind our API
Evaluate performance and reliability — moving from prompt-engineering to robust inference

You’ll be hands-on across the full ML lifecycle: data → model → evaluation → deployment.

Who You Are

You have 3–10 years of experience building production-grade ML or AI systems
Strong in Python, PyTorch, and modern ML tooling (Hugging Face, Weights & Biases, OpenAI fine-tuning APIs)
Deep understanding of LLMs, embeddings, RAG, and fine-tuning (LoRA, adapters, or custom heads)
Experience building or maintaining data pipelines and labeling systems
Can ship backend integrations (Go or TypeScript familiarity a plus)
Excited by the challenge of turning unstructured legal data into usable, scalable AI
Thrive in ambiguity, move fast, and enjoy owning problems end-to-end

Bonus

Experience in legal tech, document intelligence, or compliance AI
Familiarity with pgvector, GCP, or serverless infrastructure

Why Join

Turn a massive, real-world dataset into a competitive AI moat
Work directly with founders from Meta, GitHub, and top law firms
Ship models that go straight into customer hands — visible impact, zero bureaucracy
Competitive salary, meaningful equity, and full remote flexibility

Applied ML / LLM Engineer

Job Type