Founding Engineer, AI Platform
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Job Description
Founding engineer building Ember AI’s enterprise intelligence workspace from meetings, email, documents, Slack, and operational systems. Developing agents, connectors, workflows, and scalable platform primitives.
Responsibilities:
- Own meaningful product areas end to end, from customer discovery and technical design through implementation, launch, and iteration
- Build core Enterprise Intelligence surfaces, including the agent builder, brain pages, connectors, source transparency, calendar and meeting workflows, and internal tools
- Design reusable platform primitives for agents, including triggers, tools, approvals, evals, memory, observability, and workflow execution
- Turn messy, customer-specific workflows into reliable and extensible product experiences
- Work across the stack using React, TypeScript, Python, FastAPI, Postgres, background jobs, cloud infrastructure, LLM APIs, and retrieval systems
- Build for production, including tenant isolation, reliability, monitoring, permissions, data security, and secure connector access
- Work directly with customers through discovery calls, workflow reviews, debugging sessions, implementation, and launch support
- Use AI coding tools while maintaining engineering judgment and building understandable, operable, extensible systems
- Help shape the product roadmap, technical architecture, engineering practices, and culture
- Within 30 days, understand the product, architecture, and customer context; identify key gaps; and ship a meaningful improvement
- Within 60 days, own a core platform primitive or agent workflow end to end, including UX, reliability, evals, and observability
- Within 90 days, independently lead a customer-facing workflow or major Enterprise Intelligence surface that can scale beyond one customer
Requirements:
- Entrepreneurial builder with a 0-to-1 journey as a founder, founding engineer, or early startup employee ideally
- Built something meaningful from a blank page and can demonstrate personal decisions, tradeoffs, and outcomes
- Strong full-stack judgment across product UI, backend APIs, data models, infrastructure, and customer needs
- Comfortable with ambiguous product and platform problems
- Customer-focused approach to solving pain
- Shipped AI-powered product features in production, or strong evidence of quickly learning and building in this space
- Ability to balance speed with durability and know when to prototype, generalize, or revisit decisions
- High agency, low ego, strong product instincts, and bias toward action
- Clear communication with technical and nontechnical teammates and comfort working directly with customers
- Uses AI tools to accelerate work without outsourcing judgment
- Excited by agents, RAG, tool calling, evals, and real-business edge cases
- Nice to have: Python, TypeScript, React, Postgres, Celery or other background job systems, AWS, or comparable technologies
- Nice to have: integrations with Gmail, Slack, Google Calendar, CRMs, document systems, or other enterprise APIs
- Nice to have: LLM evals, observability, retrieval, agent orchestration, prompt reliability, or tool-use reliability
- Nice to have: secure multi-tenant enterprise software, permissions, and data access controls
- Nice to have: strong product taste and coherent, intuitive, trustworthy tools
Benefits:
- Market-competitive salary – rewarding your contributions fairly
- Global team experience – collaborate with talented professionals worldwide
- Innovative, fast-growing company where your work makes a real impact
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