Commitment:
20-40 hours/week (with 4 hours overlap with PST)
Role Responsibilities
- Design and develop multi-agent benchmark tasks involving planning, scheduling, and resource allocation. Create 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
- Strong 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
- Robust 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
- Receive Role Details & Google Form via Email
- Shortlisted Candidates Receive Interest Check Form (ICF)
- Complete the Interest Check Form, Online Assessment & Take-Home Assessment
- Final Selection & Onboarding