Software Engineer - Latam at Reacher

About Reacher

Reacher is a YC backed AI SaaS platform building the operating system for TikTok Shop brands and creators. We’re backed by Y Combinator, Pioneer Fund, Rebel Fund, and operators including Steve Huffman (CEO of Reddit), Matteo Franceschetti (CEO of Eight Sleep), Theo Browne (t3.gg), Kulveer Taggar (Zeus Living), and Jake Mintz (Bump). We have years of runway and are scaling fast with some of the biggest brands in the world (UnderArmour, Hanes, Logitech, Goli)

Our vision is to become the Hubspot for creator marketing, powering brands and creators to connect and grow across all commerce platforms (Youtube Shopping, Instagram Shopping, Shopify, Amazon). We're building key infrastructure for the creator economy and implementing AI with the world's largest brands and creators.

Your work will directly impact users on day 1 - our user base depends on our product daily.

We build with AI, not around it. Our engineering team uses Claude Code and agentic coding tools as our primary development environment. If you're spending your days directing AI agents to ship code while you focus on architecture, review, and system design — and you can't imagine working any other way — keep reading.

What You'll Do

Own and lead product features end-to-end: research, design, implement, test, and ship

Lead and shape engineering architecture - set standards for code quality, testing, and development practices across the stack

Mentor and guide other engineers in adopting AI-native workflows, establishing best practices and patterns

Talk directly to customers and translate vague requirements into polished user experiences

Design scalable backend systems (Python, FastAPI, PostgreSQL, GCP)

Build responsive, delightful UIs (React, TypeScript, Tailwind, shadcn/ui)

Write clean, maintainable, and testable code with minimal oversight

Drive architectural decisions and engineering processes that maximize AI-assisted development velocity

Ship fast and learn fast - high urgency environment

You're a Fit If

Engineering Foundations

2–6 years of professional backend leaning full-stack experience at a product-first company or FAANG-level environment (Senior SDE ownership expected)

Comfortable building features across the entire stack—from schema design and backend APIs to pixel-perfect, accessible frontend

Care deeply about product, design, and user experience—not just code

Have owned something meaningful end-to-end (a product module, infrastructure refactor, major feature launch)

Can set standards for engineering quality and architectural decisions in an AI-native environment

Resourceful and thrive in ambiguous environments—you figure things out independently

Have strong product intuition and can push back on requirements when needed to improve outcomes

AI-Native Development

AI coding experience is your most important and necessary skill - you have deep understanding of modern coding tools, specifically Claude Code, and are proficient with its various modes (Interactive, Plan, Headless, Agent Teams, Delegate)

You're "AI-pilled" when it comes to problem-solving - you tackle any new problem, skill, or architectural challenge by leveraging AI tools and an AI-driven workflow. This is your default approach, not an experiment

Strong systems thinking and architectural awareness - you direct AI to build while you architect, review, and verify. You know when AI output is correct and when it needs correction

Can demonstrate recent AI-native work - you've shipped meaningful projects in the last 90 days where AI coding tools were your primary development environment

Technical Skills We Value

AI and Agentic Development

Claude Code proficiency: understanding of modes, context engineering (CLAUDE.md), custom commands, MCP integrations, and subagent coordination

AI-driven development workflows and effective prompt engineering

Ability to decompose complex engineering work into tasks suitable for AI assistance

Strong judgment for when to leverage AI vs. when to code directly

Experience verifying and reviewing AI-generated code for correctness, security, and maintainability