Senior Research Engineer

Responsibilities

• 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.

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).
• 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.

Benefits

Additional Information

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.

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Company Intel

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