About PointOne
We’re building an AI system to automate timekeeping and firm intelligence for law firms, starting with timekeeping and billing. The business of law is rapidly changing in response to AI. We’re using our entry wedge — and the rich data stream it generates — to help firms transition their businesses into the AI era.
Our team is a mix of legal, AI, and startup backgrounds from Fenwick & West, Applied Intuition, and Google. We recently raised a seed round from Y Combinator, Bessemer, 8VC, General Catalyst, and several of our early customers (who asked to invest after using the product).
We are getting strong pull from the market and can’t keep up with the volume of customer demands — this is where you come in.
Who you are
You have 4+ years of experience in machine learning engineering, building and deploying ML systems in a fast-paced startup environment.
You have:
A CS degree from a top university
Significant experience at venture-backed startups and/or big tech companies
Deep understanding of machine learning fundamentals and experience building production ML systems
Strong product sense
High degree of ownership and the ability to operate autonomously
Demonstrated experience in growing, building, and scaling production ML systems at high-growth startups
Excitement to work in-person at an early-stage company, talk to customers, and get your hands dirty across a variety of products and domains
What you'll do
You will work with the founders and early employees to build a category-defining product, continue scaling it to massive enterprises, and become a leader in the organization.
To accomplish this, you will design, build, and ship production ML systems that drive real-world customer impact.
You’ll collaborate directly with customers to understand real-world data and needs.
You’ll design retrieval and ranking pipelines, productionize LLM applications, and optimize system performance — moving quickly from prototype to shipped feature. We put new code into production multiple times per day.
We’ll do this every day, together, in-person, because we understand that every minute counts in the early days of a startup. This is going to be intense early-stage startup work; the person we hire is expected to become a leader and help form the company’s vision and culture.
Our tech
We have a fully serverless backend built on top of AWS, consisting of a collection of Go microservices. We use React/Typescript to build client applications across web, desktop, and mobile. We create our own RAG pipelines and work extensively with LLMs and vector databases.
Day in the life
As a founding ML engineer, every day you’ll need to build fast and well with a laser-focus on customer impact. Here’s a typical day:
9:30 AM. Join a call with a managing partner at a law firm (“KAJ Law”) that recently started using PointOne. They mention their desire for more accurate task classification in our timekeeping assistant. After the call, you sketch out ideas for improving retrieval quality and prompt engineering.
11:27 AM. As we’re working, we notice a model latency issue impacting customer experience. All hands on deck to diagnose and optimize the system.
2:43 PM. You experiment with retrieval tweaks, add ranking logic, and validate improvements against internal evaluation sets. Once happy, you ship the updates to production.
3:00 PM. Lead a new-customer onboarding call, noting user feedback that could inform future improvements to ranking and recall.
5:45 PM. Team dinner. We chat about life, news, work, etc.
6:23 PM. With the big hurdles of the day overcome, you go heads-down to experiment with new models that could further enhance our automated billing assistant.