ML Engineer

About the role

About Willow

Willow is a voice-first productivity app that lets you type anywhere on your computer using your voice. People rely on Willow every day to write emails, respond in Slack, prompt AI, take notes, and move faster through their work.

We’ve proven adoption from individual power users to enterprises like Uber, Gusto, and Heidi Health. We’re backed by Y Combinator, Box Group, and founders from Instacart, Reddit, Shopify, and HubSpot.

We’re scaling quickly, and you’ll be joining early.

The Role

We’re looking for a Founding ML Research Engineer to build Willow’s personalization layer.

This role is for someone who believes the hardest unsolved problem in AI products is not raw model capability, but context and user modeling. Foundation models are getting better every month. The open question is how to build an AI system that understands a specific user in the flow of their work.

You’ll work on the systems that turn Willow from “great dictation” into a personalized communication and intent layer.

Today, a user can say something rough like: “Tell my boss I’ll come in at 6.”

Willow should understand the screen, the relationship, the app, the user’s tone, their past edits, their communication style, and produce the message the user would have written if they had taken the time to write it well.

What You’ll Work On

You’ll help build systems for:

1. Personal writing and behavior models that learn from edits, preferences, workflows, tone, formatting, and user actions

2. Intent-to-action systems that turn rough user intent into high-quality execution

3. Multimodal context understanding across voice, screenshots, app state, selected text, workflows, and historical behavior

4. Retrieval and memory systems that understand what context matters in a given moment

This is not a pure research role and not a generic ML infra role. You should be excited to take ambiguous ideas, ship quickly, learn from real-world usage, and iterate aggressively.

What We’re Looking For

You are unusually excited by personalization, context, workflows, and user modeling.

You think deeply about questions like:

1. How does an AI system learn what a user actually means?

2. How do you build systems that feel genuinely personalized instead of generically helpful?

3. How do you evaluate trust?

You probably have some combination of:

1. Strong applied ML or research engineering ability

2. Experience with LLMs, VLMs, agents, speech, personalization systems, retrieval, or multimodal systems

3. Strong product intuition and taste

We care much more about technical depth, product intuition, and speed than credentials or years of experience.

Why This Matters

The first major computer interface was the keyboard. The second was touch. The third is voice. But the opportunity is larger than voice itself.