AI Software Engineer Intern at Autohive

Wellington, Wellington, New Zealand -

Full Time

Start Date

Immediate

Expiry Date

14 Oct, 26

Salary

0.0

Posted On

16 Jul, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Backend Engineering, API Design, AI Agent Engineering, Data Modeling, Data Pipeline Construction, Telemetry Instrumentation, Systematic Reasoning, Technical Communication, Time Management, Collaboration

Industry

technology;Information and Internet

Description

About the role We're looking for enthusiastic students to join our R&D and engineering team and work on real, customer-facing product features at the frontier of AI-native software. This is a hands-on internship building the systems that let autonomous AI agents query data, reason about it reliably, and act on it. This is a fundamentally different kind of engineering challenge to traditional human-facing software. You'll work alongside experienced engineers on genuinely novel problems where the "right" answer isn't yet known. Expect real technical uncertainty, real experimentation, and the chance to see your work ship into a live commercial platform. What you'll work on Interns contribute to R&D projects across several connected areas: Agent-facing APIs and data models. Design and build API endpoints and data models that expose real-time, structured, queryable data for AI agents to consume. You'll help define what "agent-readable" actually means and build the schemas and query semantics that let agents reason reliably. AI agent engineering. Build, test and iterate on production AI agents. Work through the hard problems of reliability, tool-use and guardrails, and learn to reason systematically about failure modes in a commercial setting. AI-driven operational workflows. Develop agents and analytics that monitor, triage and escalate issues with minimal human intervention. Moving systems from simply flagging "what is wrong" toward explaining "why it matters and what to do next." Data pipelines and telemetry. Instrument product telemetry and build clean, integrated, agent-queryable data pipelines across product, usage and operational data, so agents can reason over reliable data at scale. Analyze the resulting data to draw actionable insights. Have studied or be studying at a New Zealand tertiary education institute (note: students who have completed study overseas are not eligible). Be studying at NZQA level 6-10, or if study has been completed, the closing date of the last semester must be less than 12 months ago. Be studying science, technology, engineering, design or business. Be legally permitted to work in New Zealand. Not have been previously employed at Raygun unless part-time or temporary. Not have undertaken more than two Experience Grant internships with Raygun. Be available to work in office at our Wellington CBD HQ. What you'll learn Over the internship you'll develop a mix of technical and professional skills: Backend / API engineering — design, build and test production-grade APIs and data models for a large-scale platform. AI agent and system engineering — design, build and debug reliable AI agents and reason about their failure modes. Data pipeline and analysis — build and query clean, structured data pipelines and turn data into actionable insights. Presentation and communication — present your work confidently through regular live demos to the wider business, and help create content that shares your work more broadly. Time management and collaboration — plan and manage your own work within iteration deadlines, participating in stand-ups, planning and reviews alongside the engineering team. What you'll gain Genuine exposure to commercial R&D, mentorship from experienced engineers, work that ships into a live product, and a strong foundation for a career in AI-native software engineering.

Responsibilities

Develop and iterate on production AI agents, focusing on reliability, tool-use, and guardrails. Design agent-facing APIs and data pipelines to enable autonomous agents to query and reason over real-time data.