Software Engineer

Texas Sports Academy Main

$120K — $150K *
US-AnywhereRemote in United States
Consumer Technology
5 - 7 years of experience
Job Overview by Ladders

Qualifications

  • Bachelor's or master's in Computer Science, Engineering, Math, or Physics.
  • 6+ years of full-time production engineering experience.
  • Experience in a senior, staff, or tech-lead role.
  • Daily use of AI coding tools in software development.
  • Experience shipping LLM-powered features in production with an evaluation story.
  • Strong skills in a modern web stack (TypeScript/React/Node or Python/Postgres/AWS or GCP).
  • Excellent written English.

Responsibilities

  • Ship production code and AI features for users daily.
  • Own at least one major system from end to end.
  • Rapidly develop features from concept to production.
  • Elevate AI-engineering standards as the team expands.

Benefits

  • Work directly with the founders with no PM layer.
  • Shipping code weekly, emphasizing production and delivery.
  • Exposure to cutting-edge AI tools and technologies.
  • Significant ownership of projects and systems.
  • Remote work opportunity for candidates in India or Pakistan.
Full Job Description
Software Engineer (AI-Forward)

As a Software Engineer with Texas Sports Academy, you play a key role in building 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 a senior, AI-forward seat. You work directly with the founders, ship code every week with AI in your loop, and own the LLM-powered features that make our school feel nothing like a traditional school.

What you will be doing

  • Designing, building, and shipping product features across the full stack every week, with AI coding tools running alongside you.
  • Building real LLM-powered product features: tutoring agents, parent-facing copilots, coach-facing dashboards, retrieval over student data, evals for every one of them.
  • Working directly with the founders on scope, architecture, and trade-offs - no PM layer in between.
  • Owning the reliability, security, and cost of both classical systems and LLM pipelines.
  • Running your own AI coding workflow - prompts, subagents, custom tools, MCP servers - and raising the bar on how the whole team uses AI.
  • 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 every day, this role is not for you.
  • Writing tickets for junior engineers to implement.
  • Sitting in status meetings all day - few meetings, more shipping.
  • Working on a narrow slice of a giant codebase.


Key responsibilities

  • Ship production code and AI features real students, parents, and staff rely on every day.
  • Own at least one major system end-to-end.
  • Move fast - features go from idea to production in days, not quarters - without breaking things.
  • Raise the AI-engineering bar as the team grows.

Requirements
  • Bachelor's or master's degree in Computer Science, Engineering, Math, or Physics.
  • 6+ years of full-time production engineering experience shipping software to real users at real scale.
  • At least one prior senior, staff, or tech-lead role.
  • Daily, fluent use of AI coding tools (Claude Code, Cursor, Codex, Windsurf, Aider, or equivalent) as your default way of writing software.
  • At least one shipped LLM-powered feature in production, with an eval story.
  • Strong skills in a modern web stack (TypeScript / React / Node or Python / Postgres / AWS or GCP).
  • Excellent written English
  • Legal authorization to work from India or Pakistan as a remote contractor with a US company.


Nice to have

  • Master's or PhD from a tier-1 institution.
  • Former founder, first-five engineer at a funded startup, or shipped products that reached real users at scale.
  • Agent frameworks (LangGraph, CrewAI, Mastra, custom), vector search / RAG at scale, evals (Braintrust, LangSmith, custom), prompt caching, MCP servers, structured output / tool-use reliability, voice agents.
  • Public GitHub or a personal AI project we can actually try.
  • A personal project you built because you wanted to.

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