Company
Research
SeniorResearchEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Research Engineer. Skills: memory features lifecycle, model fine-tuning, research implementation, evaluation at scale, customer pain point analysis, productionization. Own the end-to-end lifecycle of memory features—from research to production. Fine-tune models for extraction, updates, consolidation/forgetting, and conflict resolution”
What You'll Achieve.
Ship with Engineering to SOTA latency, reliability, and cost; Continuously improve quality; Productionize what wins; Validate solutions through field trials; Maintain SOTA latency, reliability, and cost at scale
Industry & Context.
turn customer pain points into research implement and benchmark ideas; turn pain points into research hypotheses
What They're Looking For.
Must Have
Experience in RAG or information retrieval (retrieval, ranking, query understanding) for real products, Model training/fine-tuning experience (LLMs/encoders) with a footing in experimental design and iteration, deep experience with PyTorch, 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
What You'll Do.
Own the end-to-end lifecycle of memory features—from research to production
Fine-tune models for extraction
consolidation/forgetting
and conflict resolution
Turn customer pain points into research implement and benchmark ideas
Ship with Engineering to SOTA latency
Build evaluation at scale (offline metrics + online As)
Close the loop with real-world feedback to continuously improve quality
and implement research
Quickly prototype paper ideas
Benchmark against baselines
Productionize what wins
Work closely with customers to uncover pain points
Turn pain points into research hypotheses
Validate solutions through field trials
Design APIs and data contracts
Maintain SOTA latency
How You'll Work.
Team & Collaboration
Partner with Engineering to ship; Work closely with customers; Clear, concise communication with stakeholders (engineering, product, GTM, and customers)
Communication Scope
Clear, concise communication with stakeholders (engineering, product, GTM, and customers)
Full Job Description
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, d
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