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 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.
Office-first collaboration
We're an in-person team based in San Francisco. Hallway conversations, whiteboard sessions, and spontaneous collaboration help us move faster and build better products than remote meetings alone.
Velocity with craftsmanship
We move quickly without sacrificing engineering excellence. Every system we build should be fast, reliable, scalable, and thoughtfully designed.
Extreme ownership
Everyone at Mem0 is a builder-owner. If you see a problem or opportunity, you're empowered to solve it. Titles matter less than impact.
High bar, high trust
We hire exceptional people, give them autonomy, and hold ourselves to a high engineering standard. We challenge ideas, review code thoughtfully, and celebrate wins together.
Data-driven, not ego-driven
The best ideas win regardless of where they come from. We rely on data, customer feedback, and thoughtful experimentation to guide decisions.
Health, dental & vision coverage - Comprehensive plans, fully covered for you (and subsidized for dependents)
Lunch & dinner, on us - Daily meals catered in-office, because good food fuels good work
Flexible PTO - Take the time you need to recharge, no rigid accrual counting
Equity in an early-stage company - Real ownership in what you're building, not just a paycheck
Regular team happy hours & events - Built into the culture, not an afterthought
Top-tier equipment - The laptop and setup you need to do your best work
Compensation Range: $175K - $250K
