About the job
Join ByteDance’s AI Agent Memory Infrastructure team, where we build the core memory systems that power next-generation intelligent agents. Our focus is on creating a unified platform for long-term, conversational, and task-oriented memory, enabling more personalized and context-aware AI experiences.
Responsibilities
- Design, build, and evolve the next-generation memory infrastructure for AI agents, developing a unified platform that supports long-term memory, conversational memory, and task-oriented memory.
- Architect and optimize memory system pipelines for large-scale, low-latency, and high-availability environments, including data ingestion, storage, indexing, retrieval, updating, compression, and forgetting mechanisms to support real-time inference and personalized interactions.
- Explore key challenges at the intersection of large language models, context engineering, and data management, including memory representation, retrieval and ranking, conflict resolution, summarization and fusion, and memory lifecycle management.
- Design unified memory models and processing workflows for multimodal data (text, image, audio, behavioral signals), enhancing agents’ long-term consistency, personalization, and task completion in complex scenarios.
- Collaborate closely with model, application, and platform teams to productionize memory capabilities, and continuously optimize system performance across quality, latency, cost, reliability, and safety.
- Stay up-to-date with cutting-edge advancements and contribute to the long-term technical roadmap of AI agent memory systems, driving innovation and capability evolution.
Qualifications
Minimum
- Bachelor’s degree or higher in Computer Science, Artificial Intelligence, Data Science, or related fields.
- Strong experience in distributed systems, databases, information retrieval systems, or AI infrastructure, with proven system design and production engineering capabilities.
- Proficient in at least one programming language such as Go, Python, or C++, with strong coding standards and engineering best practices.
- Solid understanding of core technologies in LLM applications, including but not limited to embeddings, retrieval-augmented generation (RAG), context engineering, retrieval systems, and long-term state management.
- Familiarity with one or more key areas in memory systems: memory extraction and representation, vector/graph indexing, retrieval and ranking, memory updating, compression and forgetting, multimodal memory fusion.
Preferred
- Experience in agent memory systems, user profiling, recommendation/search feature platforms, or knowledge base systems.
- Contributions to or deep understanding of open-source memory frameworks such as mem0, memOS, memU, or similar solutions.
- Strong track record in databases, information retrieval, machine learning, or AI systems, including publications, impactful open-source work, or notable technical achievements.
- Experience in multimodal data processing, online inference systems, personalized agents, or long-term user state modeling.
- Ability to analyze and optimize trade-offs across system performance, latency, cost, and scalability from both system and algorithm perspectives; experience with complex production systems is highly preferred.