Senior Research Engineer

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

• 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 language and/or retrieval and generation 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 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.

About Mem0

We're building the memory layer for AI agents. Think long-term memory that enables AI to remember conversations, learn from interactions, and build context over time. We're already powering millions of AI interactions for our enterprise customers and our open-source community (150k+ devs and counting!). We are backed by top-tier investors through a $25M Series A & Seed.

Our Culture

• Office-first collaboration - We're an in-person team in San Francisco. Hallway chats, impromptu whiteboard sessions, and shared meals spark ideas that remote calls can't.
• Velocity with craftsmanship - We build for the long term, not just shipping features. We move fast but never sacrifice reliability or thoughtful design - every system needs to be fast, reliable, and elegant.
• Extreme ownership - Everyone at Mem0 is a builder-owner. If you spot a problem or opportunity, you have the agency to fix it. Titles are light; impact is heavy.
• High bar, high trust - We hire for talent and potential, then give people room to run. Code is reviewed, ideas are challenged, and wins are celebrated—always with respect and curiosity.
• Data-driven, not ego-driven – The best solution wins, whether it comes from a founder or an engineer who joined yesterday. We let results and metrics guide our decisions.