Machine Learning Engineer

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

We’re looking for machine learning engineers who want to apply cutting-edge AI to real-world problems. Legal work is full of repetitive tasks, paper bottlenecks, and information buried in thousands of pages of unstructured data. Your job will be to help us turn that mess into clarity by building and tuning voice agents, browser agents, OCR pipelines, and LLM-powered workflows that actually work in production.

You’ll prototype quickly, run experiments, design evals, and push the best solutions into production. Expect to work closely with product, operations, and legal experts to make sure we’re solving the right problems and extracting every drop of performance from the tools at our disposal. You’ll be hands-on with everything from model selection and fine-tuning to prompt design and feedback loops, all while staying on top of what’s breaking new ground in AI so we can keep raising the bar.

What you’ll do:

• Solve Hard, Messy Problems: Build AI-powered systems that can process complex documents, handle natural conversation, and automate high-friction workflows for law firms
• Run Tight Feedback Loops: Design and execute evaluations, collect human feedback, and iterate rapidly to improve system accuracy and reliability
• Stay Cutting-Edge: Track new research, tools, and frameworks and know when to bring them into production
• Collaborate Deeply: Partner with product, ops, and legal experts to ensure solutions fit real-world needs
• Ship With Confidence: Build for safety, observability, and performance from day one
• Work Onsite: Join us in-person 4 days/week in NYC for high-context, high-speed collaboration

You Might Be a Fit if You:

• Have 3+ years building and deploying production ML systems (bonus if in NLP, OCR, speech, or agents)
• Are comfortable moving across the stack (we use Python, AWS, LangChain, OpenAI APIs, React, and more)
• Write clean, maintainable, and observable code and care about both experiments and production stability
• Thrive in ambiguous environments and know how to make progress without a perfect spec
• Are excited to work on applied AI where small improvements can unlock huge value for end users
• Care about outcomes over algorithms—you’re here to solve problems, not just chase benchmarks

Compensation

$170,000 - $250,000 annual salary + generous equity

Additional benefits include:

• 100% coverage for health, dental, and vision
• $500/month tooling stipend for productivity and dev tools
• In-office snacks, drinks, and daily team dinners
• Flexible PTO (we trust you to take the time you need)

At Finch Legal, we believe in practicing what we advocate.

As a firm dedicated to upholding justice and protecting people in the workplace, we are equally committed to fostering a safe, inclusive, and equitable environment within our own walls. We welcome and support individuals from all backgrounds and lived experiences — regardless of race, ethnicity, gender identity, sexual orientation, religion, disability, or veteran status.

We recognize that diversity strengthens our team, enriches our perspectives, and empowers us to better serve our clients and communities. At Finch Legal, inclusion isn’t just a value — it’s a practice.

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