Full Stack Engineer, AI systems at Bjak

Palo Alto, California, United States -

Full Time

Start Date

Immediate

Expiry Date

27 Jun, 26

Salary

0.0

Posted On

29 Mar, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Full Stack Engineering, Frontend, Backend, AI Integrations, Agent Workflows, LLMs, RAG Systems, System Design, API Architecture, Real-time AI Interactions, Observability, Next.js, Python, NodeJs, Pytorch, Kubernetes

Industry

Software Development

Description

About the Role A1 is building a proactive AI chat app for everyday users to bring intelligence to conversations, errands, organising and workflows. Unlike traditional chat-based applications, our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior. We are looking for a Full Stack Engineer - AI Systems to build the product layer that turns these capabilities into usable, production-grade workflows. This includes designing 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 under real-world conditions Design real-time AI interactions with streaming, partial results, and tight latency constraints Improve system reliability, observability, and fallback mechanisms Collaborate closely with ML, backend, and product teams to ship features end-to-end Continuously iterate based on real usage and failure modes Ideal Experiences Strong experience in full stack engineering (frontend + backend) Solid understanding of system design and API architecture Experience working with LLMs, RAG systems, or AI-powered applications Ability to handle ambiguity and make pragmatic engineering decisions Strong ownership - able to take features from idea to production Comfort working in fast-moving environments with evolving requirements Outcomes Own and ship AI-native product features that move 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 of AI interactions while maintaining output quality Build robust fallback and recovery mechanisms for LLM and tool failures in production environments Improve the success rate and reliability of AI-driven workflows through iteration, evaluation, and monitoring Establish patterns and abstractions for integrating LLMs, memory, and external tools into scalable product systems Contribute to a product experience where AI feels proactive, consistent, and dependable over time Tech Stack Next.js Python NodeJs Pytorch OpenAI / Anthropic / open-source LLMs SQl & noSQL Kubernetes Docker How We Work The best products today in the world were built by small, world class teams. We are a high talent density and hands-on team. We make decisions collectively, move at rapid speed, striking a balance between shipping high quality work and learning. Joining our team requires the ability to bring structure, exercise judgment, and execute independently. Our goal is to put in hands of our users a truly magical product Interview process If there appears to be a fit, we'll reach to schedule 3, but no more than 4 interviews. Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite. We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us. This isn't just a job offer; it's an invitation to be part of a team that's bringing AI to have practical benefits to billions globally.

Responsibilities

The engineer will be responsible for building the product layer that transforms AI capabilities into production-grade, usable workflows, focusing on designing how agents operate, recover from failures, and deliver consistent value to users. This includes building end-to-end features across the stack, integrating LLMs and external tools, and improving system reliability and observability.