Engram builds AI models that learn from your context and deeply understand your work. Backed by General Catalyst, Kleiner Perkins and Sequoia.
About the role
You’ll be among the first ML Systems Engineer, joining a team of machine learning researchers and performance engineers in building our personalization and continual learning API, which powers models and agents that learn from user context. This role is focused on designing, optimizing, and scaling training and inference workloads —bridging the gap between cutting-edge AI research and production. This includes:
What they're looking for
- Prior early-stage experience
- Experience in open-source ML or systems infrastructure projects
- Engram is based in San Francisco. This role is in-person in our SF office. We offer competitive cash compensation and startup equity
More about this role
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.
You’ll be among the first ML Systems Engineer, joining a team of machine learning researchers and performance engineers in building our personalization and continual learning API, which powers models and agents that learn...
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