Back-End Software Engineer (Co-op / Extended Internship)

ABOUT ENGRAMME

Engramme is a Frontier AI lab spun out of Harvard focusing on AI x Human Memory. Persistent memory is a fundamental open problem at the heart of AI. Our vision is to build models of human memory, and to endow humans with perfect and infinite memory.

Before starting Engramme, the founding team has produced and commercialized extensive research in machine learning and neuro-science inspired memory models:

- Gabriel Kreiman (CEO & Co-founder): Professor of Neuroscience and AI, Harvard Medical School. Led key research on brain memory consolidation at Harvard University.

- Spandan Madan (CTO & Co-founder): PhD in Computer Science from Harvard, designed AI models which were commercially deployed at Adobe, Fujitsu, and Meta.

WHAT YOU’LL DO

Join our engineering team to build the backbone of our Memory-as-a-Service platform. You’ll architect scalable systems that power contextual memory retrieval for millions of users.

- Design and implement scalable backend architecture for our memory platform

- Build robust APIs that handle millions of memory retrievals per day

- Optimize database performance and implement caching strategies

- Collaborate with the engineering team on technical architecture decisions

- Establish engineering best practices and code quality standards

- Work with AI/ML teams to integrate memory retrieval algorithms

- Monitor system performance and implement automated scaling solutions

WHAT WE’RE LOOKING FOR

- Strong proficiency in Python and modern web frameworks (FastAPI, Django, Flask)

- Experience with PostgreSQL and database optimization

- Knowledge of cloud platforms (AWS, GCP) and containerization (Docker, Kubernetes)

- Understanding of caching systems (Redis, Memcached) and distributed architectures

- Experience with API design and microservices architecture

- Strong problem-solving skills and attention to detail

- Experience with CI/CD pipelines and infrastructure as code

NICE TO HAVE

- Experience with AI/ML infrastructure and vector databases

- Knowledge of similarity search and embedding systems

- Background in high-performance computing or real-time systems

- Previous startup or early-stage company experience

- Open source contributions

- Experience with monitoring tools (Prometheus, Grafana)

- Knowledge of message queues (RabbitMQ, Apache Kafka)