The future of technical hiring

Hire engineers who use AI efficiently

TokenHire gives every candidate the same AI token budget. How efficiently they use it — alongside correctness and speed — determines their rank.

Evaluate candidates across any stack

⚛️React

🐍Python

🟩Node.js

📘TypeScript

⚙️C++

☕Java

🐹Go

🦀Rust

🗄️SQL

🐳Docker

⚛️React

🐍Python

🟩Node.js

📘TypeScript

⚙️C++

☕Java

🐹Go

🦀Rust

🗄️SQL

🐳Docker

⚛️React

🐍Python

🟩Node.js

📘TypeScript

⚙️C++

☕Java

🐹Go

🦀Rust

🗄️SQL

🐳Docker

Watch them work.

As candidates solve problems, they can use our built-in AI assistant. But every prompt costs tokens.

The Token Budget.

A physical limit on AI dependence. Watch the budget drain. Will they exhaust it immediately, or use it surgically?

The True Score.

Correctness isn't enough anymore. We rank engineers by a composite score of speed, accuracy, and AI efficiency.

Tokens: 1000/1000

Ask AI

vector<int> twoSum(vector<int>& nums, int target) {

// I need to optimize this O(n^2) loop...

for(int i = 0; i < nums.size(); i++) {

for(int j = i + 1; j < nums.size(); j++) {

if(nums[i] + nums[j] == target) return {i, j};

}

// AI optimized: Use unordered_map to achieve O(n) time complexity

}

✨AI Assistant

Instead of a nested loop, you can use `std::unordered_map` to store complements and solve this in O(n) time.

- 700 tokens used

🏆

Test Completed

Candidate #402

87.5

Composite Score

Correctness100%

Speed80%

AI Efficiency30%

Watch them work.

The ultimate candidate evaluation simulation.

Tokens: 300/1000

87.5

Final Composite Score

⚡

Same AI for everyone

Every candidate gets identical token budget. No unfair advantage.

📊

Token efficiency score

Candidates who solve problems with fewer AI calls rank higher.

🎯

Adjustable weights

Set your own scoring formula — optimise for speed, correctness, or efficiency.