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

Interview Process

• Meet with Mariam
• Meet with Samir (technical Q&A)
• Meet with Sona, CEO
• Architecture Design Review (technical deep dive into a project you’ve done)