Full Stack Engineer, AI systems
Bjakcareer · United States
About this role
## About ACTAI ACTAI is building proactive applications for the 5B+ users who rely on basic tools like email, notes, tasks, and calendars—but aren’t AI-native. The mission: bring intelligence to conversations, errands, organizing, and workflows with minimal to no prompting. The product focuses on **high reliability for long-running workflows**, **persistent context**, and **real-world task completion**, with the belief that better product design can **reduce hallucinations**. --- ## Role: Full Stack Engineer — AI Systems Build the product layer that turns AI capabilities into **usable, production-grade workflows**. You’ll help design how agents **operate, fail, recover, and deliver consistent value** to users. --- ## Focus - Build **end-to-end product features** across frontend, backend, and AI integrations - Design **agent workflows** that handle planning, tool use, failure, and recovery across multiple steps - Integrate **LLMs, memory, and external tools** into systems that behave reliably in real-world conditions - Design **real-time AI interactions** with streaming, partial results, and tight latency constraints - Improve **reliability, observability, and fallback mechanisms** - Collaborate with **ML, backend, and product** teams to ship features end-to-end - Iterate based on real usage and failure modes --- ## Ideal Experiences - Strong full stack engineering experience (**frontend + backend**) - Solid understanding of **system design** and **API architecture** - Experience with **LLMs, RAG systems, or AI-powered applications** - Ability to handle ambiguity and make pragmatic engineering decisions - Strong ownership (take features from idea to production) - Comfortable in fast-moving environments with evolving requirements --- ## Outcomes - Own and ship **AI-native product features** beyond chat into persistent, goal-driven workflows - Design and deploy **agent workflows** that reliably complete multi-step tasks across tools and sessions - Reduce latency and improve responsiveness while maintaining output quality - Build robust **fallback and recovery** mechanisms for LLM/tool failures in production - Improve success rate and reliability through **iteration, evaluation, and monitoring** - Establish scalable patterns/abstractions for integrating LLMs, memory, and external tools - Contribute to an experience where AI feels **proactive, consistent, and dependable** over time --- ## Tech Stack - Next.js - Python - Node.js - PyTorch - OpenAI / Anthropic / open-source LLMs - SQL & NoSQL - Kubernetes - Docker --- ## How We Work Small, world-class teams with high talent density. Decisions are collective and execution is hands-on, balancing **shipping quality** with **learning**. You’ll be expected to bring structure, exercise judgment, and execute independently. --- ## Interview Process - If there’s a fit, you’ll be scheduled for **3 interviews** (no more than 4) - Interviews are virtual and/or onsite - Expect a **prompt decision** focused on transparency and efficiency --- *If you’re looking to build AI that delivers practical benefits to billions, this is an invitation to join a team making that real.*
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