Senior Software Engineer, AI

About the role

Purpose in Every Position

Pebl puts a world of talent at your fingertips. With our AI-powered Global Work Platform™, companies can hire, pay, and manage employees in 185+ countries—removing risk, red tape, and guesswork from global growth. Backed by more than a decade of compliance leadership and local expertise, Pebl helps businesses move fast, stay compliant, and scale with confidence. With Pebl, companies everywhere can hire great talent anywhere.

Where Your Work Moves the Needle.

At Pebl, every team member drives our success. Joining us means more than filling a role—you’re accountable for results and impact. Our values guide how we operate, execute, and collaborate across borders. By putting them into action, you’ll help us scale faster, compete harder, and lead the future of global work.

What Makes You a Great Fit

Our mission at Pebl is to simplify global employment, enabling companies to hire, manage, and retain talent anywhere in the world. We’re not just adding a chatbot to our product — we’re rethinking what it means for software to work with you, not just for you. Our AI & Knowledge team is building Alfie, our conversational AI assistant, and we’re looking for a Senior Full-Stack AI Engineer who wants to help define what AI-native software looks like, not follow a playbook that doesn’t exist yet.

This is a true full-stack role. You’ll own features end to end: the APIs, data models, and services behind our LLM-based product, and the conversational interface people actually use. You’re equally comfortable designing a tool-calling contract and shaping what a user sees when the model gets something wrong. You have hands-on experience building LLM-based and agentic products, and you’ve shipped something real end users, not just developers, talk to. This role reports to the Engineering Manager, with day-to-day work led by our Staff Software Engineer.

How You’ll Make an Impact:

- Design, build, and ship full-stack features for Alfie, with ownership from the data model and API layer through to the user experience.

- Build and operate backend (micro)services in TypeScript or Python, Postgres, and AWS, deployed on Kubernetes, including clean contracts and APIs that support agentic workflows. Occasionally work with Lambda for async or infrequent, non-user-facing processing.

- Build the frontend in React/TypeScript, including conversational and streaming interfaces, and bring product taste to what makes an AI-driven experience great rather than shipping “good enough.”

- Apply hands-on experience with LLM-based and agentic systems — prompt design, tool/function calling, multi-step agent reasoning, evaluation — to shape both how the assistant behaves and how that behavior surfaces to the user.

- Design for the moments AI gets wrong: recovery paths, human-in-the-loop handoffs, and what users see when the assistant is uncertain or fails.

- Prototype and propose new interaction patterns as the underlying models and agent capabilities evolve.