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This listing was closed on Aug 19, 2026.
Browse Open JobsResearch Engineer - Agent Memory
Summary by AIEngineer.careers
Research Engineer - Agent Memory is a AI Research Engineer role (full-time). with Mem0. in SAN FRANCISCO, US. Compensation shown: $175K–$250K. Imported listing (source: zerogtalent.com). Apply on the employer's site (zerogtalent.com).
Imported listing (source: zerogtalent.com) · Apply on zerogtalent.com
SAN FRANCISCO · US
$175K–$250K
Full-time
SENIOR
The sections below reproduce the third-party job description for reference. AIEngineer.careers does not write or control this text.
Imported job description
Sourced from zerogtalent.com
Mem0 is building the memory layer for AI agents, enabling long-term memory for AI interactions. As a Research Engineer, you will fine-tune models for memory extraction, updates, and consolidation, turning research ideas into production features within weeks. You'll own the evaluation layer and ship models at SOTA latency and reliability, working in-office in San Francisco.
Responsibilities
- Train models: Memory extraction, updates, consolidation/forgetting, and conflict resolution, iterating fast on real data and real outcomes.
- Live at the research frontier: Turn paper ideas into working prototypes in days, benchmark them honestly against baselines, and productionize the ones that actually win.
- Build the evaluation layer: Automated relevance/accuracy/consistency metrics, gold sets, online A/B and interleaving tests, and dashboards people actually check.
- Get close to the problem: Uncover what's actually breaking for customers, turn it into a testable hypothesis, and validate it through real field trials.
- Ship it for real: Design APIs and data contracts, plan safe rollouts, and hit SOTA latency, reliability, and cost, with Engineering, not over the wall from them.
Requirements
- Experience in RAG or information retrieval (retrieval, ranking, query understanding) for real products.
Mem0
San Francisco · US · 23+ employees
Mem0 is an AI company that provides a persistent memory layer for AI agents and applications. Its platform enables AI to continuously learn from past user interactions, enhancing personalization and intelligence while reducing context-related costs.
