Mem0
AI
AppliedAIEngineer
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Applied AI Engineer at Mem0. Skills: Applied AI, Full-stack prototyping, LLM/RAG stack, Memory retrieval experimentation. Own the 0→1.. Turn vague customer use cases into working proofs-of-concept.”
What You'll Achieve.
Showcase what Mem0 can do.; Hit task-level quality and latency targets.; Hand off winning prototypes that can be hardened for production.; Productionize quickly.
Industry & Context.
Turn vague customer use cases into working proofs-of-concept.; Experiment with memory retrieval approaches until the use case works end-to-end.; Ability to design small, meaningful evaluations for a use case (quality + latency) and iterate based on evidence.
Office-first collaboration, In-person team in San Francisco
What They're Looking For.
Must Have
Full-stack fluency: Next. js/React on the front end and Python backends (FastAPI/Django/Flask) or Node where needed., Python and TypeScript/JavaScript; comfortable building APIs, wiring data models, and deploying quick demos., Hands-on with the LLM/RAG stack: embeddings, vector databases, retrieval strategies, prompt engineering., Track record of rapid prototyping: moving from idea → demo in days, not clear documentation of results and trade-offs., Ability to design small, meaningful evaluations for a use case (quality + latency) and iterate based on evidence., Excellent communication with Research and crisp specs, readable code, and honest status updates.
Nice to Have
Model serving/fine-tuning experience (vLLM, LoRA/PEFT) and lightweight batch/async pipelines., Deployments on Vercel/serverless, Docker, basic k8s CI for demo apps., Data visualization and UX polish for compelling demos., Prior Forward-Deployed/Solutions/Prototyping role turning customer needs into working software.
What You'll Do.
Turn vague customer use cases into working proofs-of-concept.
Rapid full-stack prototyping.
Stitch together AI tools.
Aggressively experiment with memory retrieval approaches.
Build POCs for real use cases.
Stand up end-to-end demos (UI + APIs + data).
Experiment with memory retrieval (embeddings
Implement paper ideas and new techniques from scratch.
Create eval harnesses.
Integrate AI tooling (LLMs
third-party services).
Package & handoff prototypes.
How You'll Work.
Team & Collaboration
Partner closely with Research and Backend.; Communicate trade-offs clearly.; Hand off winning prototypes.; Prototype with Research.; Collaborate tightly with Backend on clean contracts and data.; Collaborate with Research on share learnings and next steps.
Communication Scope
Communicate trade-offs clearly; Excellent communication with Research; Crisp specs; Readable code; Honest status updates
Process & Methodology
Moving from idea → demo in days, Documentation of results and trade-offs
Full Job Description
Role Summary: Own the 0→1. You’ll turn vague customer use cases into working proofs-of-concept that showcase what Mem0 can do. This means rapid full-stack prototyping, stitching together AI tools, and aggressively experimenting with memory retrieval approaches until the use case works end-to-end. You’ll partner closely with Research and Backend, communicate trade-offs clearly, and hand off winning prototypes that can be hardened for production. What You'll Do: - Build POCs for real use cases: Stand up end-to-end demos (UI + APIs + data) that integrate Mem0 in the customer’s flow. - Experiment with memory retrieval: Try different embeddings, indexing, hybrid search, re-ranking, chunking/windowing, prompts, and caching to hit task-level quality and latency targets. - Prototype with Research: Implement paper ideas and new techniques from scratch, compare baselines, and keep what wins. - Create eval harnesses: Define small gold sets and lightweight metrics to judge POC success; instrument demos with basic telemetry. - Integrate AI tooling: Combine LLMs, vector DBs, Mem0 SDKs/APIs, and third-party services into coherent workflows. - Collaborate tightly: Work with Backend on clean contracts and data models; with Research on hypotheses; share learnings and next steps. - Package & handoff: Write concise docs, scripts, and templates so Engineering can productionize quickly. Minimum Qualifications - Full-stack fluency: Next.js/React on the front end and Python backends (FastAPI/Django/Flask) or Node where needed. - Strong Python and TypeScript/JavaScript; comfortable building APIs, wiring data models, and deploying quick demos. - Hands-on with the LLM/RAG stack: embeddings, vector databases, retrieval strategies, prompt engineering. - Track record of rapid prototyping: moving from idea → demo in days, not months; clear documentation of results and trade-offs. - Ability to design small, meaningful evaluations for a use case (quality + latency) and iterate based on evidence. -
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