Role Summary
Build the product surface of Mem0 Platform, our memory platform powering memory for AI Agents. You'll own features end-to-end across Next.js and Python, shipping fast without compromising code quality, performance, or reliability. You'll partner with design, research, and customers to turn real problems into elegant, scalable product experiences.
This isn't a standard SaaS dashboard. You're building the interface and APIs through which developers configure, inspect, and debug long-term memory for AI agents. Every component you build, every API you design, and every interaction you polish directly shapes how developers experience memory infrastructure.
What You'll Ship Early
First 30 days: Ship your first end-to-end feature to production. Understand the full stack, the deployment pipeline, and how customers actually use the platform.
First 60 days: Own a product workstream. You have opinions on what to build next based on what you've seen from customers and the codebase.
First 90 days: Be a core product builder the team relies on. You're shipping features independently, improving developer experience, and making the platform faster and more reliable.
What You'll Do
Ship end-to-end features. Design APIs, build UIs, write backend logic, own data models, and deploy to production.
Build for scale and speed. Optimize latency, caching, and query patterns. Keep pages snappy and backends reliable.
Own quality. Write tests (using coding agents 🙂 ), enforce typing/linting, review PRs, and maintain clean, well-documented code.
Collaborate deeply. Work with Design for great UX, with Research to integrate new memory capabilities, and with customers to refine requirements.
Operate what you build. Add observability, set alerts, debug prod issues, and drive continuous improvements.
Lead with product sense. Prioritize ruthlessly, make tradeoffs explicit, and iterate based on data and feedback.
Go beyond your lane. Unblock teams, learn new tools on the fly, and do what it takes to deliver.
Minimum Qualifications
Proven experience shipping full-stack web applications at scale using Next.js/React and Python.
Strong Python skills and familiarity with modern web stacks (REST/GraphQL, Postgres, Redis, Celery/queues).
Solid front-end chops: component architecture, state management, forms, accessibility, SSR/ISR.
Track record of owning features end-to-end, from design docs to rollout and post-launch iteration.
Code quality mindset: testing (unit/integration), type safety (TS/pyright/mypy), CI/CD, and thoughtful review culture.
Excellent communication and teamwork. Comfortable working cross-functionally with design, research, and GTM.
Comfortable operating production systems (logs, metrics, tracing) and meeting low-latency requirements.
No CS degree required. We care about impact and craftsmanship.
Nice to Have
Experience integrating LLM/memory features (RAG, embeddings, vector DBs) into products.
Familiarity with vLLM, model serving, or lightweight ML infra integrations.
Real-time features (WebSockets/Server Actions/streaming), file uploads, and background jobs at scale.
Infra awareness: Docker, IaC, basic K8s/Vercel/AWS/GCP deployment patterns and cost thinking.
Product/UI polish: Tailwind/Design Systems, charts, empty states, error UX, and performance budgets.
Security and privacy basics (PII handling, audit/logging).
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.