The Role

We are seeking somebody with a background in building AI and intelligent systems at the intersection of Large Language Models (LLMs) and Operations Research (OR).

You will develop AI that understands business problems, formulates optimization models, and interacts with solvers to support real-world applications. This role balances research (developing new ideas) with engineering (building robust prototypes and product features).

Responsibilities

• Build LLM-based systems and pipelines for problem understanding, model formulation, and code generation.
• Design and run experiments, benchmarks, and error analysis to improve system reliability and scalability.
• Translate technical research into working prototypes and production-ready features.
• Collaborate with the team on decision-support applications for enterprise clients.

Requirements

• Bachelor’s or Master’s in Computer Science, Operations Research, Math, Data Science, AI, or a related field.
• Strong programming ability with a focus on clean, modular, and reliable code.
• Solid mathematical background, specifically in optimization problem solving.
• Ability to work independently in a fast-paced startup environment with evolving priorities.
• Excellent communication skills for discussing technical concepts with cross-functional teammates.

Technical Expertise

1. Optimization & Modeling

• Experience with LP, MIP, NLP, or constraint programming.
• Ability to translate business problems into mathematical models (variables, objectives, constraints).
• Familiarity with solvers like Gurobi, CPLEX, SCIP, or OR-Tools.
• Understanding of solver behavior, computational bottlenecks, and model debugging.
• Experience with practical problems such as routing, scheduling, or resource allocation.

2. Engineering & AI

• Expertise in code structure, testing, logging, and building reproducible experiment pipelines.
• Ability to integrate AI components with external tools, APIs, and enterprise systems.
• Strong judgment on system performance, scalability, and failure handling.
• Knowledge of LLM workflows (prompting, retrieval, agents), machine learning evaluation, and structured code generation.

Tokka Labs does not accept unsolicited resumes.

Any form of candidate introduction shared without the prior approval of the talent acquisition team, will be deemed free to contact by Tokka Labs without restriction or liability. No placement fee of any kind will be paid in the event the identified candidate is hired by Tokka Labs.