About the role
About Engram
Today’s AI is a brilliant stranger: it can solve the world’s hardest math problems, but it knows next to nothing about you and your work. It rereads your files to answer even basic questions, burns an enormous amount of tokens when sifting through large corpuses, and between sessions, it retains scraps at best.
We train models to study your world and anticipate your questions in advance, forming engrams: compact memories that capture your knowledge and history. Our approach opens a new axis of scaling. The more we study your context at training time, the better we become at inference time.
We're already working with leaders in AI like Microsoft, Notion, and Harvey, and just raised $98M from General Catalyst, Kleiner Perkins, Sequoia, Factory, Modern, Amplify, Neo and others. Our investors and advisors include Assaf Rappaport, Andrej Karpathy, and Pieter Abbeel.
AI has spent years learning everything about the world. Now it should learn something about yours.
If you’re interested in working with us, but don't see an open role that matches your skillset, we'd still like to hear from you! Filling out this form lets us know you're open to hearing from us down the road about roles or opportunities that might be a great fit for you.
About Engram
Today’s AI models don’t understand what you do. Not really. Everything models know comes from their training – and they’re trained mostly on the public internet. They’re knowledgeable about popular Github repos and things people write in articles online. But what you spend your time thinking about every day is so much more than that. You know what good work looks like. You know where a specific project is going, and where you want it to be in a year. You know everything – from the small details, to the big picture of how life and work fit together – knowledge that goes far beyond any chat window. And when you do write things down, important ideas scatter across a sprawl of documents and files. When you use a model, it reads and re-reads many documents from your company before it can even get started. But as we all know by now, this paradigm isn’t perfect: as our contexts grow, models become more expensive and more confused. Individual users already generate far too much data for a model to process. And reading is shallow and temporary; even when the model does see your context, it forgets everything the moment you close the chat. Right now, models do not learn from this data. This means they can’t automatically get better at the things you use them for. We want to change this by building models that learn from your context. Ours is a fundamentally different bet from other labs. Instead of spending massive amounts of training compute on public data, we start from strong pre-trained models and spend training compute on the context you care about. Each model spends the equivalent of hundreds of years studying your context: piecing things together, drawing connections that have never been drawn, finding errors that went unnoticed.
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Job details
Location
San Francisco, CA
Funding
Total raised
$98M
Last stage
Seed
Investors
Founders
What happens next.
No applications, no recruiter spam. Just the intro.
Confirm the fit
A few questions to make sure this role is the right shape for you. Two minutes.
I pitch you to the company
I write the intro, send it to the founder, and handle the back-and-forth.
A meeting lands on your calendar
If they’re a yes, I book the chat. You show up — that’s the whole job-hunt.