Mem0

The memory layer for Personalized AI

Senior Research Engineer

Research EngineerResearch EngineerFull TimeRemoteTeam 12Since 2023

Location

California

Posted

34 days ago

Salary

Not specified

Bachelor Degree9 yrs expEnglishPythonPy TorchVllm

Job Description

Role Summary: Own the end-to-end lifecycle of memory features—from research to production. You’ll fine-tune models for extraction, updates, consolidation/forgetting, and conflict resolution; turn customer pain points into research hypotheses; implement and benchmark ideas from papers; and ship with Engineering to SOTA latency, reliability, and cost . You’ll also build evaluation at scale (offline metrics + online A/Bs) and close the loop with real-world feedback to continuously improve quality. What You'll Do: Fine-tune and train models for memory extraction, updates, consolidation/forgetting, and conflict resolution; iterate based on data and outcomes. Read, reproduce, and implement research : quickly prototype paper ideas, benchmark against baselines, and productionize what wins. Build evaluation at scale : automated relevance/accuracy/consistency metrics, gold sets, online A/B & interleaving, and clear dashboards. Work closely with customers to uncover pain points, turn them into research hypotheses, and validate solutions through field trials. Partner with Engineering to ship : design APIs and data contracts, plan safe rollouts, and maintain SOTA latency, reliability, and cost at scale. Minimum Qualifications

Job Requirements

  • Experience in
  • RAG
  • or
  • information retrieval
  • (retrieval, ranking, query understanding) for real products.
  • Model training/fine-tuning
  • experience (LLMs/encoders) with a strong footing in experimental design and iteration.
  • Strong
  • Python
  • ; deep experience with
  • PyTorch
  • and familiarity with
  • vLLM
  • and modern serving frameworks.
  • Built
  • evaluation
  • for complex
  • vision-and-language
  • tasks (gold sets, offline metrics, online tests).
  • Able to
  • orchestrate data pipelines
  • to run these models in production with
  • low-latency
  • SLAs (batch + streaming).
  • Clear, concise
  • communication with stakeholders
  • (engineering, product, GTM, and customers).
  • Nice to Have:
  • Publications at venues like
  • CVPR
  • ,
  • NeurIPS
  • ICML
  • ACL
  • , etc.
  • Experience with
  • privacy-preserving ML
  • (redaction, differential privacy, data governance).
  • Deep familiarity with
  • memory/retrieval
  • literature or prior work on memory systems.
  • Expertise with
  • embeddings
  • , vector-DB internals,
  • deduplication
  • , and contradiction detection.

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