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Engram Lab

Learned memory layer for enterprise AI

Platform Engineer

Full-Time
No salary listed
Senior
San Francisco, CA, USA
In Person
DevOps & InfrastructurePlatform Engineering

About the job

Requirements
  • At least 5 years of software engineering experience building and scaling systems in a high-stakes environment.
  • Experience building application programming interfaces or infrastructure consumed by other developers, including software development kits, multi-tenant platforms, or developer tooling.
  • Experience taking a successful product or open-source project from zero to one.
  • Experience working in a rigorous engineering culture where security and reliability are top priorities.
Responsibilities
  • Architect and implement the data infrastructure underpinning the personalization and continual learning service.
  • Design a secure data storage architecture for raw user context and per-user model weights.
  • Develop performant systems for regularly ingesting and preprocessing data for continual training.
  • Build data observability for quality control and reproducibility.
  • Create reliable integrations with customer application programming interfaces and other data sources.
  • Act as a major architect of the system while collaborating with machine learning researchers, performance engineers, and customers.
  • Set engineering standards through code review, testing, on-call practices, and a security posture that gives customers confidence in entrusting the company with sensitive data.
  • Help build the engineering team and influence engineering culture as the company scales.
Desired Qualifications
  • Background working with machine learning teams and familiarity with the modern machine learning serving and training stack.
  • Prior early-stage experience.
  • Product-driven approach and willingness to work across the stack.

About the company

Engram Lab builds AI systems that learn from a user's context to understand their work. Its core product is a learned memory layer that separates organizational knowledge from model reasoning so enterprise AI workloads don’t reprocess full context for every query. The company starts from strong pre-trained models and trains on the customer’s context, making the models token-efficient and able to draw connections across documents, workflows, and institutional knowledge. Its goal is to continually absorb data with a single training algorithm, aiming to retrain hourly (and eventually every minute) to reduce inference costs and improve personalization for large enterprise LLM workloads.

Company Size

11-50

Company Stage

Early VC

Total Funding

$98M

Headquarters

San Francisco, California

Founded

2025

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Simplify's Take

What believers are saying

  • June 2026, Engram raised $98 million from Sequoia, Kleiner Perkins, and General Catalyst.
  • Microsoft committed GPU capacity across Dapple and Azure for Engram training.
  • Notion reported order-of-magnitude token savings using Engram inside custom agents.

What critics are saying

  • Engram had no public product in June 2026, so adoption depends on pilots.
  • Microsoft, Notion, and Harvey build internal alternatives, collapsing Engram's distribution by 2027.
  • Hourly and minute-level retraining demands unsolved scaling; failure kills the thesis.

What makes Engram Lab unique

  • June 2026, Engram's learned memory layer compresses organizational context into reusable model memory.
  • Microsoft 365, Notion, and Harvey already test Engram across enterprise workflows.
  • The 13-person research team includes Stanford's Chris Ré and memory specialist Natalie Biderman.

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Benefits

  • Company Equity

Growth & Insights and Company News

Headcount

6 month growth

-5%

1 year growth

-5%

2 year growth

-5%
The SaaS News
Jun 24th, 2026
Engram Raises $98M in Funding

Engram emerges from stealth with $98M to build a learned memory layer for AI that helps organizations create more efficient and context-aware agents.