Role Summary
Own the 0 to 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.
You'll be one of the first Applied AI hires, working directly with the founders and a small research + backend team.
What You'll Ship Early
First 30 days: Build and demo a working POC for a real customer use case. UI, APIs, data pipeline, Mem0 integration, end-to-end.
First 60 days: Own two to three active POC workstreams, establish eval harnesses, and develop a repeatable playbook for turning customer problems into Mem0-powered demos.
First 90 days: Be the go-to person for applied prototyping at Mem0. You'll have shipped multiple demos, contributed retrieval improvements back to the core product, and built templates that accelerate future POCs.
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 and 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 to 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.
Excellent communication with Research and Backend. 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 familiarity. CI for demo apps.
Data visualization and UX polish for compelling demos.
Prior Forward-Deployed/Solutions/Prototyping role turning customer needs into working software.
Compensation
Salary: $150K to $250K base (depending on experience)
Equity: 0.10% to 0.50%
Location: San Francisco (in-person)
About Mem0
We're building the memory layer for AI agents. Long-term memory that lets AI remember conversations, learn from interactions, and build context over time. We already power millions of memory operations daily across companies building AI-native products.
Mem0 is a Y Combinator (S24) company, backed by top-tier investors including Peak XV and Basis Set Ventures. We raised $24M to make this the default memory infrastructure for AI.
The Founders
Deshraj Yadav, Co-founder and CTO. Led the AI Platform at Tesla Autopilot, enabling large-scale training, model evaluation, and observability for Tesla's full self-driving development. MS in CS from Georgia Tech (ML specialization). Created EvalAI as his master's thesis, an open-source ML evaluation platform used by researchers at CMU, Stanford, Facebook, and Google. Published at CVPR, ECCV, AAAI.
Taranjeet Singh, Co-founder and CEO. Started as a software engineer at Paytm, then built an AI-powered tutoring app at Gradeup (acquired by Byju's) that was featured at Google I/O. Joined Khatabook (YC S18) as first growth engineer and became Senior PM. Built cookup.ai, the first GPT app store, and scaled it to 1M+ users with zero marketing spend. Co-authored an O'Reilly book chapter on industrial NLP alongside researchers from Google AI, CMU, and Microsoft Research.
Together, Deshraj and Taranjeet co-created EvalAI and later built Embedchain, an open-source RAG framework with 2M+ downloads. While building Embedchain, they saw firsthand how LLMs forget everything between sessions, leading to repetitive, impersonal interactions. Mem0 was born to fix that: a hybrid memory architecture combining graph, vector, and key-value stores that makes AI applications stateful, personalized, and cost-efficient.
How We Work
Office-first in SF. Hallway chats, whiteboard sessions, and shared meals. The best ideas happen in person.
Velocity with craftsmanship. We ship fast but build for the long term. Every system needs to be fast, reliable, and elegant.
We debug retrieval quality over lunch. Half our Slack is embedding comparisons. If you've ever argued about chunk sizes at 11pm, you'll fit right in.
Data-driven, not ego-driven. The best solution wins, whether it comes from a founder or an engineer who joined yesterday. Results and metrics guide decisions.
Small team, big leverage. You'll work directly with the founders and a tight research + backend team. No layers, no committees.