HQUS
Team Size10
Open Jobs10
Total Funding-
Latest FundraiseUnknown
What they do: Provide a production-ready memory layer that compresses and indexes conversational facts for LLM applications
Location & size: San Francisco; ~10 employees
Compliance & deployment: Advertises SOC 2 and HIPAA compliance with deploy-anywhere options
Funding signal: Raised $24M across Seed and Series A (Series A led by Basis Set Ventures)
AI infrastructure / memory for large language model applications
Artificial intelligence / AI infrastructure
Pre-seed recorded on company profiles
Series A recorded on company profiles; reporting combines seed + Series A into $24M total
“Seed led by Kindred Ventures; Series A led by Basis Set Ventures with participation from Peak XV Partners, GitHub Fund, and Y Combinator; additional angels listed”
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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.