Tech Lead, Software Engineer - AI Agent Memory Infrastructure

What you'll do

• Design and evolve a unified memory platform and its ingestion, storage, indexing, retrieval, updating, compression, and forgetting pipelines.
• Develop memory models and workflows for retrieval, ranking, conflict resolution, summarization, and multimodal fusion.
• Collaborate with model, application, and platform teams to productionize memory capabilities and optimize quality, latency, cost, reliability, and safety.

What they ask for

• Bachelor’s degree or higher in Computer Science, Artificial Intelligence, Data Science, or a related field.
• Strong experience in distributed systems, databases, information retrieval systems, or AI infrastructure, with system design and production engineering capabilities.
• Proficiency in at least one of Go, Python, or C++.
• Understanding of LLM application technologies such as embeddings, retrieval-augmented generation (RAG), context engineering, retrieval systems, and long-term state management; familiarity with at least one key area of memory systems.

Nice to have

• Experience with agent memory systems, user profiling, recommendation/search feature platforms, or knowledge base systems; familiarity with open-source frameworks such as mem0, memOS, or memU.
• Experience in multimodal data processing, online inference systems, personalized agents, or long-term user state modeling; relevant publications, open-source work, or technical achievements.

Tools

Go, Python, C++, LLMs, embeddings, retrieval-augmented generation (RAG), mem0, memOS

AI focus

Develops AI agent memory systems, including long-term and multimodal memory, retrieval and ranking, memory lifecycle management, and production integration with LLM-powered agents.