# Senior Research Engineer

**Company:** [Mem0](https://hotfix.jobs/companies/mem0)
**Location:** San Francisco, CA
**Role:** ML Engineering
**Salary:** $175k – $250k/yr
**Experience:** 7+ years
**Skills:** Python, PyTorch, vLLM, RAG, Information Retrieval, Llm Fine-Tuning, Model Training, Evaluation Metrics, Data Pipelines, Vector Databases
**Posted:** 2026-06-19

> Own the end-to-end lifecycle of memory features for AI agents. Fine-tune models, implement research, build evaluations, and ship production systems with Engineering.

## Job Description

## 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 language and/or retrieval and generation 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 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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