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.
Skills Required
- Experience in RAG or information retrieval for real products.
- Model training/fine-tuning experience with LLMs/encoders and strong experimental design.
- Strong Python programming skills.
- Deep experience with PyTorch and familiarity with vLLM and modern serving frameworks.
- Built evaluation for complex vision-and-language tasks including gold sets, offline metrics, and online tests.
- Ability to orchestrate data pipelines for production models with low-latency SLAs (batch and streaming).
- Clear, concise communication with engineering, product, GTM, and customers.
- Publications at top ML/vision/NLP conferences (CVPR, NeurIPS, ICML, ACL).
- Experience with privacy-preserving ML (redaction, differential privacy, data governance).
- Deep familiarity with memory/retrieval literature or prior memory systems work.
- Expertise with embeddings, vector-DB internals, deduplication, and contradiction detection.
What We Do
The memory layer for Personalized AI
