Member of Technical Staff, Applied AI

Finch•New York, NY

18h•$180,000 - $280,000•Onsite

About The Position

Finch is building the infrastructure to make justice radically more accessible, starting with personal injury. Our modern approach to consumer law automates administrative work and prioritizes clients. In just over a year, we've grown 10x, raised a $20M Series A, and become the pre-litigation partner of choice for top personal injury firms nationwide. We believe the best outcomes occur when expert operators and purpose-built AI collaborate. We handle every step of pre-litigation, from intake and claim opening to medical records, lien management, and demands, with humans leading every case. We are backed by prominent investors and are rebuilding how the law serves everyday Americans from first principles, seeking exceptional operators to help us scale nationwide.

Requirements

3+ years building reliable, secure software in production, with the ability to own systems across the stack.
Experience shipping AI-powered products or agents beyond the prototype stage.
Experience designing practical evals and using them to understand agent behavior, compare approaches, and improve task-completion quality.
Proficiency in Python and comfort working with React, LLM APIs, and agent frameworks.
Strong instincts for agent design, including context construction, model selection, tool boundaries, structured outputs, failure recovery, and human handoffs.
A track record of turning ambiguous, real-world workflows into simple, dependable systems.
An experimental mindset: you move quickly, measure what matters, and care more about whether an agent completes the job correctly than whether the approach is clever.

Nice To Haves

Experience with Pydantic AI or another production agent framework.
Experience building voice agents, browser agents, or long-running workflow agents.
Familiarity with Django, React, Temporal, Playwright, or AWS.
Experience building evaluation systems, agent observability, model routing, or human-in-the-loop review tools.
Familiarity with legal tech, healthcare workflows, document processing, or other operationally complex domains.

Responsibilities

Build production-grade voice, browser, document, and workflow agents that move personal injury cases forward.
Design long-horizon agent systems - including context, tools, memory, permissions, structured outputs, and human-in-the-loop controls.
Own the eval flywheel: turn real cases and operator feedback into benchmarks that continuously improve agent quality.
Optimize performance through model selection and routing, context engineering, prompting, tool design, and agent observability.
Make agents reliable across calls, documents, communications, third-party portals, partial failures, and changing case information.
Evaluate advances in agentic AI and rapidly ship the ones that improve quality, speed, or cost.