Applied ML / LLM Engineer

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 Apply tot his job