Senior Research Engineer
Role Overview
As a Senior Research Engineer, you will lead the end-to-end development of memory features, from research to production, by fine-tuning models for extraction, updates, and conflict resolution. You'll prototype and benchmark ideas from research papers, build scalable evaluation systems, and collaborate with engineering teams to ensure state-of-the-art performance. This role involves close work with customers to identify pain points and validate solutions, driving significant improvements in product quality and reliability.
Perks & Benefits
This is a fully remote position, offering flexibility in work location and likely accommodating various time zones for collaboration. You'll have opportunities for career growth through hands-on research, publication possibilities, and working with cutting-edge technologies in AI and machine learning. The role emphasizes a collaborative culture with cross-functional teams, including engineering and product, fostering innovation and real-world impact.
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, 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.
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