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.