Junior Software Engineer - AI-Forward

Finch; Austin, Northern (TX, KY); Hybrid; USD 70,000 - 110,000; Full time

Job summary

Texas Sports Academy is hiring a Junior Software Engineer to help build the school’s software, student records, and AI-powered tools used by guides and coaches. This is an early-career, AI-forward seat.

You’ll ship features weekly with AI in your loop, collaborating directly with founders and senior engineers to grow into LLM-powered capabilities that modernize education.

Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, Math, or Physics.
• 0 to 2 years of full-time engineering experience.
• Daily, fluent use of AI coding tools as the default way of writing software.
• Comfortable in a modern web stack (TypeScript / React / Node or Python / Postgres / AWS or GCP).
• Excellent written English.

Responsibilities

• Ship production code and AI features that real students, parents, and staff rely on every day.
• Own smaller systems end-to-end as you ramp up.
• Move fast. Features go from idea to production in days, not quarters.
• Level up your AI-engineering chops alongside a senior team.

Skills

0-2 years exp

AI coding tools

English fluency

Full-stack development

Education

Bachelor's or Master's in CS/Engineering/Math/Physics

Tools

TypeScript

React

Node

Python

Postgres

AWS

GCP

Job description

As a Junior Software Engineer with Texas Sports Academy, you'll help build the software that runs our school, student records, academic mastery tracking, training data, parent portals, admissions, and the AI-powered tools our guides and coaches use every day. This is an early-career, AI-forward seat. You'll work directly with the founders and senior engineers, ship code every week with AI in your loop, and grow into the LLM-powered features that make our school feel nothing like a traditional school.

What You Will Be Doing

• Building and shipping product features across the full stack every week, with AI coding tools running alongside you.
• Contributing to real LLM-powered product features: tutoring agents, parent-facing copilots, coach-facing dashboards, retrieval over student data, and the evals behind them.
• Working directly with the founders and senior engineers on scope and trade-offs, no PM layer in between.
• Picking up ownership of smaller systems end-to-end and growing into bigger ones.
• Running your own AI coding workflow, prompts, subagents, custom tools, MCP servers, and getting sharper at it every week.
• Writing evals and regression tests for AI features the same way you'd write unit tests for classical code.

What You Will NOT Be Doing

• Pretending AI is optional. If you're not already coding with Claude Code, Cursor, Codex, or an equivalent agent loop, this role is not for you.
• Sitting in status meetings all day. Few meetings, more shipping.
• Working on a narrow slice of a giant codebase.
• Waiting around for tickets. You'll be in the room when we decide what to build.

Key Responsibilities

• Ship production code and AI features that real students, parents, and staff rely on every day.
• Own smaller systems and features end-to-end as you ramp up.
• Move fast. Features go from idea to production in days, not quarters, without breaking things.
• Level up your AI-engineering chops alongside a senior team.

• Bachelor's or master's degree in Computer Science, Engineering, Math, or Physics.
• Based in Austin, TX (or within commuting distance)
• 0 to 2 years of full-time engineering experience. Strong internships, side projects, and shipped personal work count.
• Daily, fluent use of AI coding tools (Claude Code, Cursor, Codex, Windsurf, Aider, or equivalent) as your default way of writing software.
• Comfortable in a modern web stack (TypeScript / React / Node or Python / Postgres / AWS or GCP).
• Excellent written English.

Nice to Have

• At least one shipped LLM-powered project, school project, hackathon, or side project, with some kind of eval story.
• Agent frameworks (LangGraph, CrewAI, Mastra, custom), vector search / RAG, evals (Braintrust, LangSmith, custom), prompt caching, MCP servers, structured output / tool-use, or voice agents.
• Public GitHub or a personal AI project we can actually try.
• A personal project you built because you wanted to.
• Background in education, edtech, or sports.