ByteDance

1 day ago

Software Engineer - AI Agent Memory Infrastructure

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 23 hours agoStatus: Live

Job Summary

Build and evolve ByteDance’s AI Agent Memory Infrastructure, designing a unified long-term, conversational, and task-oriented memory platform for intelligent agents. Architect low-latency, highly reliable memory pipelines covering ingestion, storage, indexing, retrieval, updates, compression, and forgetting for real-time inference. Tackle memory representation, retrieval/ranking, conflict resolution, summarization/fusion, and multimodal memory workflows, partnering with model and product teams to productionize and optimize quality, latency, cost, reliability, and safety.

Location: San Jose

Workplace: Onsite

Employment Type: Full time

Job Function: Software Engineering

Key Responsibilities

•Design, build, and evolve next-generation memory infrastructure for AI agents, supporting long-term, conversational, and task-oriented memory.
•Architect and optimize memory system pipelines for large-scale, low-latency, high-availability environments, including ingestion, storage, indexing, retrieval, updating, compression, and forgetting for real-time inference.
•Address challenges at the intersection of LLMs, 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) to improve consistency, personalization, and task completion.
•Collaborate with model, application, and platform teams to productionize memory capabilities and optimize performance across quality, latency, cost, reliability, and safety.

Key Requirements

•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.
•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 LLM application technologies including embeddings, retrieval-augmented generation (RAG), context engineering, retrieval systems, and long-term state management.
•Familiarity with memory systems areas such as memory extraction/representation, vector/graph indexing, retrieval/ranking, memory updating, compression/forgetting, and multimodal memory fusion.

Education:Bachelor's in Computer Science, Artificial Intelligence, Data Science

Tech Stack:GoPythonC++LLMsEmbeddingsRetrieval-augmented generation (RAG)Context engineeringRetrieval systemsLong-term state managementVector/graph indexingMem0MemOSMemU

Company Brief

ByteDance

Develops consumer internet and content platforms, including TikTok and other apps for short-form video, news, and entertainment. It also builds advertising, commerce, and creator tools that connect audiences, brands, and publishers across global markets.

Industry: Digital Media

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Established Company

Headquarters: Beijing, China

Founded: 2012

Website LinkedIn

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ByteDance

1 day ago

Software Engineer - AI Agent Memory Infrastructure

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 23 hours agoStatus: Live

Job Summary

Location: San Jose

Workplace: Onsite

Employment Type: Full time

Job Function: Software Engineering

Key Responsibilities

•Design, build, and evolve next-generation memory infrastructure for AI agents, supporting long-term, conversational, and task-oriented memory.
•Architect and optimize memory system pipelines for large-scale, low-latency, high-availability environments, including ingestion, storage, indexing, retrieval, updating, compression, and forgetting for real-time inference.
•Address challenges at the intersection of LLMs, 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) to improve consistency, personalization, and task completion.
•Collaborate with model, application, and platform teams to productionize memory capabilities and optimize performance across quality, latency, cost, reliability, and safety.

Key Requirements

•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.
•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 LLM application technologies including embeddings, retrieval-augmented generation (RAG), context engineering, retrieval systems, and long-term state management.
•Familiarity with memory systems areas such as memory extraction/representation, vector/graph indexing, retrieval/ranking, memory updating, compression/forgetting, and multimodal memory fusion.

Education:Bachelor's in Computer Science, Artificial Intelligence, Data Science

Tech Stack:GoPythonC++LLMsEmbeddingsRetrieval-augmented generation (RAG)Context engineeringRetrieval systemsLong-term state managementVector/graph indexingMem0MemOSMemU

Company Brief

ByteDance

Industry: Digital Media

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Established Company

Headquarters: Beijing, China

Founded: 2012

Website LinkedIn