AI Native Software Engineer

About the role

Terros builds software that helps field sales teams manage their work, understand performance, and serve customers more effectively. You’ll work closely with our CTO and engineering team to build features, improve existing software, and solve problems that matter to customers. AI coding tools will be a regular part of your work. Our technology is built across a modern cloud and mobile stack: backend primarily on AWS with TypeScript, mobile apps native Swift (iOS) and Kotlin (Android). You should be comfortable learning across the stack and using AI tools to become productive quickly. Experience building on AWS is especially valuable. You’ll work on a real product with direct access to the people making technical and product decisions. You’ll receive technical guidance and feedback, with responsibilities matched to your demonstrated ability and increasing as you learn. We don’t expect you to arrive knowing our entire codebase. We do expect you to take an active role in learning it and become increasingly effective at delivering reliable work. Location requirement: you must currently live in Southern California and be able to work in person at our Irvine office (this is not a remote position; we are considering local applicants only).

Responsibilities

• Build and improve the product: turn customer needs into practical features, fix defects, and follow changes through testing and release.
• Use AI thoughtfully: give coding agents useful context, evaluate their suggestions, and recognize when their output needs correction.
• Take initiative: ask why a problem matters, suggest simpler approaches, and identify useful improvements rather than waiting for every step to be prescribed.
• Work with the team: participate in code reviews, communicate progress and uncertainty, ask for help when needed, and apply feedback to your next change.
• As you become familiar with the product, help troubleshoot customer issues and participate in the engineering support rotation.

Requirements

• Evidence that you can build: you have created or meaningfully improved something with code (does not need to be commercially successful, publicly available, or technically elaborate—but you should be able to explain how it works and what you did).
• Practical experience with AI coding tools: you’ve used modern AI coding tools to do more than generate isolated snippets; you can describe how you worked through a real task, what you delegated to AI, what the tools got right or wrong, and how you verified the result.
• Curiosity and resourcefulness: when something is unfamiliar, you investigate, experiment, and ask useful questions; you don’t need to know everything already, but you want to understand more than the immediate answer.
• Initiative and follow‑through: you notice problems, propose solutions, and finish agreed work; when you get stuck or discover a risk, you communicate rather than letting it sit.
• Openness to feedback: you can reconsider an approach, explain your reasoning, and improve your work without becoming attached to the first solution.
• No formal degree or years of experience required; a computer science degree is not required and there is no minimum number of years of professional experience. Experience may come from a job, internship, internship, coursework, freelancing, or personal projects.