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Engram Lab

Learned memory layer for enterprise AI

Platform Engineer

Platform EngineeringConfirmed live in the last 24 hours
Full-timeSan Francisco, CA, USA

About the Job

Requirements

  • At least 5 years of software engineering experience building and scaling systems in a high-stakes environment.
  • Experience building APIs or infrastructure consumed by other developers, such as software development kits, multi-tenant platforms, or developer tooling.
  • Experience taking a successful product or open-source project from zero to one.
  • Product-driven approach and enjoyment of working across the stack.
  • Experience in a rigorous engineering culture where security and reliability are top priorities.

Responsibilities

  • Architect and implement the data infrastructure underpinning the 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.
  • Develop reliable integrations with customer APIs and other data sources.
  • Set engineering standards through code review, testing, on-call practices, and a security posture that gives customers confidence in entrusting sensitive data.
  • Help build the engineering team and influence engineering culture as the company scales.
  • Collaborate with machine learning researchers and performance engineers to build the personalization and continual learning API.
  • Work in deep collaboration with customers and shape architecture around research and development constraints and requirements.

Desired Qualifications

  • Background working with machine learning teams and familiarity with the modern machine learning serving and training stack.
  • Prior early-stage experience.

About the Company

Engram Lab

Learned memory layer for enterprise AI

Engram Lab builds AI systems that learn from a user’s context to understand their work. Its learned memory layer separates organizational knowledge from model reasoning so enterprise AI workloads don’t reprocess full context for every query. Rather than training on public data, Engram starts from strong pre-trained models and trains on a customer's own documents and workflows, making the model more token-efficient and able to act like a teammate who already knows the work. Its goal is a single training algorithm that continually absorbs data, aiming for faster retraining and lower inference costs for enterprise LLM workloads, positioning itself in AI infrastructure and inference optimization as an alternative to ever-larger context windows and repeated retrieval.

Company size
11-50
Company stage
Early VC
Total funding
$98M
Headquarters
San Francisco, CA
Founded
2025

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