ByteDance

2 days ago

Senior 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: 35 minutes agoStatus: Live

Job Summary

Build and evolve AI agent memory infrastructure that powers long-term, conversational, and task-oriented intelligence. Design distributed, low-latency, high-availability pipelines for ingestion, storage, indexing, retrieval, updating, compression, and forgetting. Tackle memory representation, retrieval/ranking, conflict resolution, summarization, and multimodal fusion across text, image, audio, and behavioral signals. Partner with model, application, and platform teams to productionize memory capabilities and optimize quality, latency, cost, reliability, and safety.

Location: San Jose

Workplace: Onsite

Employment Type: Full time

Job Function: Software Engineering

Seniority: Mid level

Key Responsibilities

•Design, build, and evolve next-generation memory infrastructure for AI agents, supporting long-term, conversational, and task-oriented memory in a unified platform.
•Architect and optimize memory system pipelines for large-scale, low-latency, high-availability environments, covering ingestion, storage, indexing, retrieval, updating, compression, and forgetting.
•Work on memory challenges across LLMs, context engineering, and data management, including representation, retrieval and ranking, conflict resolution, summarization, fusion, and lifecycle management.
•Design unified memory models and processing workflows for multimodal data (text, image, audio, behavioral signals) to improve long-term consistency and personalization.
•Collaborate with model, application, and platform teams to productionize memory capabilities and optimize 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 production engineering and system design capabilities.
•Proficiency in at least one programming language such as Go, Python, or C++ with strong coding standards.
•Solid understanding of core LLM application technologies, including embeddings, retrieval-augmented generation (RAG), context engineering, retrieval systems, and long-term state management.
•Experience with memory systems concepts like memory extraction/representation, vector/graph indexing, retrieval and ranking, memory updating, compression/forgetting, and multimodal memory fusion.

Education:Bachelor's

Tech Stack:GoPythonC++LLMsEmbeddingsRetrieval-augmented generation (RAG)Context engineeringRetrieval systemsVector indexingGraph indexingMultimodal dataMultimodal fusion

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

2 days ago

Senior 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: 35 minutes agoStatus: Live

Job Summary

Location: San Jose

Workplace: Onsite

Employment Type: Full time

Job Function: Software Engineering

Seniority: Mid level

Key Responsibilities

•Design, build, and evolve next-generation memory infrastructure for AI agents, supporting long-term, conversational, and task-oriented memory in a unified platform.
•Architect and optimize memory system pipelines for large-scale, low-latency, high-availability environments, covering ingestion, storage, indexing, retrieval, updating, compression, and forgetting.
•Work on memory challenges across LLMs, context engineering, and data management, including representation, retrieval and ranking, conflict resolution, summarization, fusion, and lifecycle management.
•Design unified memory models and processing workflows for multimodal data (text, image, audio, behavioral signals) to improve long-term consistency and personalization.
•Collaborate with model, application, and platform teams to productionize memory capabilities and optimize 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 production engineering and system design capabilities.
•Proficiency in at least one programming language such as Go, Python, or C++ with strong coding standards.
•Solid understanding of core LLM application technologies, including embeddings, retrieval-augmented generation (RAG), context engineering, retrieval systems, and long-term state management.
•Experience with memory systems concepts like memory extraction/representation, vector/graph indexing, retrieval and ranking, memory updating, compression/forgetting, and multimodal memory fusion.

Education:Bachelor's

Tech Stack:GoPythonC++LLMsEmbeddingsRetrieval-augmented generation (RAG)Context engineeringRetrieval systemsVector indexingGraph indexingMultimodal dataMultimodal fusion

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