ByteDance

2 days ago

Tech Lead, 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: 1 hour agoStatus: Live

Job Summary

Lead the engineering of AI agent memory infrastructure—designing and evolving a unified platform for long-term, conversational, and task-oriented memory. Architect low-latency, high-availability pipelines spanning ingestion, storage, indexing, retrieval, updating, compression, and forgetting, while tackling challenges in memory representation, retrieval/ranking, and multimodal fusion. Partner with model, application, and platform teams to productionize capabilities and continuously optimize performance across 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.
•Architect and optimize memory pipelines for large-scale, low-latency, high-availability environments across ingestion, storage, indexing, retrieval, updating, compression, and forgetting.
•Address challenges at the intersection of LLMs, context engineering, and data management, including representation, retrieval/ranking, conflict resolution, summarization and fusion, and 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 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 proven system design and production engineering.
•Proficiency in at least one programming language such as Go, Python, or C++ with strong coding standards.
•Solid understanding of LLM application technologies including embeddings, RAG, context engineering, retrieval systems, and long-term state management.
•Familiarity with memory systems such as extraction/representation, vector/graph indexing, retrieval and ranking, updating, compression/forgetting, and multimodal memory fusion.

Experience:Distributed systemsDatabasesInformation retrievalLLM applicationsAI infrastructure

Education:Bachelor's in Computer Science, Artificial Intelligence, Data Science (or related fields)

Skills:System designProduction engineeringOptimizationCollaborationRoadmap contribution

Tech Stack:GoPythonC++LLMsEmbeddingsRetrieval-augmented generation (RAG)Context engineeringVector indexingGraph 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

2 days ago

Tech Lead, 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: 1 hour 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.
•Architect and optimize memory pipelines for large-scale, low-latency, high-availability environments across ingestion, storage, indexing, retrieval, updating, compression, and forgetting.
•Address challenges at the intersection of LLMs, context engineering, and data management, including representation, retrieval/ranking, conflict resolution, summarization and fusion, and 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 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 proven system design and production engineering.
•Proficiency in at least one programming language such as Go, Python, or C++ with strong coding standards.
•Solid understanding of LLM application technologies including embeddings, RAG, context engineering, retrieval systems, and long-term state management.
•Familiarity with memory systems such as extraction/representation, vector/graph indexing, retrieval and ranking, updating, compression/forgetting, and multimodal memory fusion.

Experience:Distributed systemsDatabasesInformation retrievalLLM applicationsAI infrastructure

Education:Bachelor's in Computer Science, Artificial Intelligence, Data Science (or related fields)

Skills:System designProduction engineeringOptimizationCollaborationRoadmap contribution

Tech Stack:GoPythonC++LLMsEmbeddingsRetrieval-augmented generation (RAG)Context engineeringVector indexingGraph 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