Algorithms and Competitive Programming Expert

Vraify; Deutschland; Remote; EUR 30,000 - 49,000; Full time

Job summary

Vraify is seeking contributors for remote, project-based work focused on advanced algorithms and robust testing. You will create, solve, and review tasks, test AI-generated code, and document failures with reproducible evidence. Strong English communication and practical depth in algorithms are essential for success.

Remote opportunities pay up to $40/hour and depend on project scope and demonstrated expertise, with a flexible schedule for experienced contributors.

Qualifications

• Strong practical depth in algorithm design, complexity, and original problem construction.
• Ability to design adversarial test cases and validate solutions thoroughly.
• Excellent written English for clear technical communication.

Responsibilities

• Create, solve, or review realistic tasks involving algorithm design and problem construction.
• Test AI-generated code and agent traces for correctness and edge cases.
• Write strong tests, patch references, expected outcomes, and concise failure explanations.
• Review peer-authored tasks for realism, reproducibility, and evaluation criteria.

Skills

Algorithm design

Complexity analysis

Adversarial test cases

Solution validation

Clear written English

Tools

GitHub

Automated testing frameworks

Job description

Help us find where advanced coding agents break in algorithms and competitive programming. Build or solve realistic tasks, stress-test generated code, and explain failures with reproducible evidence. Remote project-based work can pay up to $40/hour, depending on the project and demonstrated expertise.

What you'll do:

• Create, solve, or review realistic tasks involving algorithm design, complexity, original problem construction, constraints, adversarial test cases, and solution validation.
• Test AI-generated code and agent traces for correctness, edge cases, regressions, unsafe assumptions, and incomplete fixes.
• Write strong tests, reference patches, expected outcomes, and concise explanations of the failure mode.
• Review peer-authored tasks for realism, reproducibility, difficulty, and clean evaluation criteria.

What we look for:

• Strong practical depth in algorithm design, complexity, original problem construction, constraints, adversarial test cases, and solution validation.
• Ability to navigate unfamiliar code or specifications and communicate technical decisions in clear written English.
• Verifiable evidence such as meaningful GitHub work, shipped software, a competition profile, technical writing, or a portfolio. Confidential industry work can be described in the application without exposing proprietary material.
• Prior AI-evaluation experience is useful but not required; strong engineering judgement and testing discipline matter more.