About Edexia

Edexia is on a mission to fix the mess we call the education system. We're starting with assessment - the area that places the greatest burden on educators and offers the most immediate impact for positive change. By revolutionising how assessment works, we're creating a cascade of improvements that will transform education from the ground up.

While others in EdTech are content with wrapping a basic UI around existing LLMs, we're taking the opposite approach - building sophisticated AI systems that actually solve the hardest problems in education (look at “What You'll Build” to find out more).

Our vision extends far beyond assessment. We're creating the foundation for systemic educational change, addressing the fundamental misalignments and inefficiencies that have plagued schools for generations. We've already secured contracts with schools across Australia and the US we raised $4M USD at a $35M valuation after completing Y Combinator.

About Us

We're a small, focused 3 person team based in Brisbane, Australia. The CEO, Daniel Gibbon, and CTO Nathan want are both second-time technical founder who previously built an EdTech to 200k ARR at 18 in just 1 year while achieving a perfect GPA. Now we are all on a mission rebuild the education system from the ground up with AI.

This Job is Perfect for You If

• You're excited about solving challenging technical problems that have direct, meaningful impact on education and are wiling to work relentless to ward a central vision.
• You value being part of a highly intelligent, driven team that pushes each other to excel

Must Haves

• Be incredibly smart, good at solving complex problems and learning new things quickly.
• Be willing to work relentlessly to accomplish a vision of rebuilding our education system from the group up with AI
• Genuinly care about having a positive impact through your career.
• Have good basline life and work habits and a willingness to reletelsly optimsing every aspect of your life to optimse your productive and furfuilment
• Have some form of technical background, even if that is just math or physics.
• Have the confidence to keep pace with some of the smart most driven people in the world

Nice to Haves

• Domain expertise in software engineering.
• Domain expertise in AI/ML
• Domain expertise in education
• Historical achicement that backup your intellgicular abilities (e.g. IMO/IOI medals)

What You'll Build

As part of our team, you'll work on aspects of these challenges that both:

1. Move the needle most significantly for our company and mission
2. Align with your interests and strengths - whether that's core AI architecture, UI/UX design, reinforcement learning systems, or data pipeline engineering

Assessment is our strategic starting point. To provide significant impact in this area, we need to solve 2 core technical challenges, after which we can continue to work on the infinite number of technical challenges to ultimately rebuild education. The 2 core current technical challenges are:

1. The Human-AI Alignment Problem: Interpreting Rubrics

• Problem: Grading rubrics include vague terms like "informed" versus "adequate," resulting in inconsistent, inaccurate, and biased evaluation from both humans and LLMs. We need to extract teachers' existing conscious and unconscious interpretation of these variables and help them further define them into precise evaluation parameters.
• Solutions:
  • Rubric unpacking workflow to make a sophisticated initial guess of the interpretation
  • Real-time voice AI rubric training coaches that have conversations with teachers to align on interpretation
  • Reinforcement learning components that extract patterns from teacher corrections

2. The Complex Marking Process: Accurately Evaluating Student Work

• Problem: Assessment requires making hundreds of discrete decisions about student work that could be in any format (typed, handwritten, graphs, equations) and cover any subject area. Each parameter of analysis could be evaluating anything from mathematical validity to grammar to logical coherence.
• Solutions:
  • Multi-agent workflows that break down complex assessment tasks into processing, identification, analysis, and communication
  • OCR and computer vision systems that can handle diverse formats
  • Task-specific analysis modules for different types of evaluation
  • Systems for converting AI judgments into clear, actionable feedback

Both challenges require a sophisticated two-level optimization approach:

1. System architecture level: Breaking down complex problems into flexible multi-agent infrastructures that subdivide the work intelligently
   • End-to-end evaluation frameworks to test complete performance
2. Individual agent level: Optimizing each specific component for its particular task
   • Unit-level testing for each component and decision point
   • When optimizing individual agents, we often do the following:
     • First, we evaluate and combine existing models on the market in intelligent ways
     • We experiment with different task decompositions and prompting techniques
     • When needed, we explore fine-tuning and reinforcement learning from feedback
     • As a last resort, we develop our own custom models for specific tasks

To power ongoing improvement, you'll help build our data engine like Tesla's autonomous driving fleet, we're creating systems that collect and leverage real-world usage

Tech Stack

• Python for AI development and model integration
• Various LLM APIs (OpenAI, Anthropic, etc.)
• Custom OCR and computer vision components
• Reinforcement learning frameworks
• Web technologies for the frontend (handled primarily by our CTO)
• Cloud infrastructure for deployment and scaling