Responsibilities of the Candidate:

Implement well-scoped features and components for agents, copilots, and platform services, with guidance from senior engineers and our architect.
Write clean, tested code, and build the evaluations that show an agent behaves the way it should—before and after it ships.
Integrate tools and data sources into agentic workflows, using open standards such as the Model Context Protocol.
Contribute to the internal AI platform: agent runtimes, model access, evaluation harnesses, and developer tooling.
Learn and apply modern AI engineering practice—prompting, retrieval-augmented generation, tool use, evaluation-first development, and observability for systems whose output is not deterministic.
Partner with teammates and business stakeholders to turn requirements into working software, and take part in code reviews, design discussions, and on-call as you ramp.

Requirements:

Engineering Foundation: Roughly 1–3 years of software engineering experience, or a strong new graduate with internships or substantial project work in software and applied AI/ML.
Language Fundamentals: Solid fundamentals in at least one production language (Python, Go, TypeScript, or Java), and willingness to work across our stack rather than in one corner of it.
Real AI Curiosity: Exposure to LLMs, prompting, retrieval, agents, and tool use—through coursework, internships, or things you built yourself. We care that you have actually made something work.
A Growth Mindset: You ask good questions, learn fast, and move without waiting for perfect information.
Clear Communication: You can explain your work to engineers and to non-engineering stakeholders, and you collaborate well across time zones.