Machine Learning Engineer

Finch Legal•New York City, NY

7d•Onsite

About The Position

About Finch We believe every American household deserves access to counsel in life’s biggest moments. At Finch, we’re building the infrastructure to make justice radically more accessible. Our modern approach to consumer law automates the admin work and puts clients first, starting with personal injury. Since launching in April, we’ve grown 10x and are now powering pre-litigation for top law firms across the country. We combine expert operators with purpose-built AI to handle intake, claim opening, medical records, police reports, lien management, demands, and everything in between. We’re backed by Sequoia, Redpoint, and the founders & CEOs of generational companies like DoorDash, Ironclad, and Digits. We’re rebuilding how the law serves everyday Americans from first principles, and we’re hiring exceptional operators to help us scale it nationwide. This Role Legal work is buried in unstructured documents, repetitive workflows, and data that no existing system handles well — and we're building the AI to fix it. As a Machine Learning Engineer at Finch, you'll own the full lifecycle of AI systems, from prototype to production, working on problems where a single breakthrough can meaningfully change how law firms operate. What You'll Do This is a hands-on applied AI role where you'll build and ship production systems — not just run experiments. The scope will grow as our product and team do.

Requirements

3+ years building and deploying production ML systems.
Strong Python skills and experience working across the ML stack end-to-end.
Hands-on experience with LLMs, prompt engineering, and evaluation design.
A track record of shipping observable, maintainable AI systems — not just prototypes.

Nice To Haves

Experience with NLP, OCR, speech, or agent frameworks (LangChain, OpenAI APIs, etc.).
Prior work at an early-stage startup where you helped define ML infrastructure from scratch.
Familiarity with legal tech, document-heavy workflows, or regulated industries.

Responsibilities

Build voice agents, browser agents, OCR pipelines, and LLM-powered workflows that work reliably in production.
Design rigorous evaluation frameworks and feedback loops to systematically improve model accuracy and reliability.
Own the full ML lifecycle — model selection, fine-tuning, prompt design, deployment, and monitoring.
Collaborate directly with product, ops, and legal experts to make sure the AI is solving the right problems.
Track emerging research and tools, and make deliberate calls about when to bring them into our stack.