Role Responsibilities

Design and develop multi-agent benchmark tasks involving planning, scheduling, and resource allocationCreate real-world operational scenarios (logistics, project management, incident response, capacity planning)

Build constraint-rich problem statements with multiple dependencies and variables

Develop Python-based scripts to evaluate feasibility, completeness, and optimality

Break down complex problems into structured sub-tasks for multi-agent systems

Model scenarios with timelines, dependencies, and resource constraints

Collaborate with teams to improve task quality, coverage, and evaluation rigor

Requirements

5+ years of experience in operations, project management, logistics, or supply chain

Strong understanding of constraints, dependencies, and scheduling logic

Proficiency in Python for validation and verification scripting

Strong structured problem-solving and decomposition skills

Ability to model real-world operational scenarios

Clear technical communication and documentation skills

Ability to work in a fast-paced environment and meet deadlines

Preferred Qualifications

Experience with optimization techniques (linear programming, constraint satisfaction, scheduling algorithms)

Background in operations research

Experience with simulation or modeling tools

Familiarity with AI planning systems or automated reasoning

Experience with AI benchmarks (e.g., SWE-bench, Terminal-bench)

Hands-on experience with Docker

Application Process

Apply via Easy Apply / shared link and complete the Interest Check Form (ICF)

Complete the take-home assessment (post-shortlisting)

Shortlisted candidates will be reviewed further

The team will connect with next steps

Compensation: $15/hour