全职 Artificial Intelligence Researcher 工作, 薪水, Alpha Z 公司招聘中 - Ricebowl

About Alpha-Z

Alpha-Z specialises in building AI for complex decision-making and problem solving. Built for human judgement, our AI supports the decision process whilst our people provide domain expertise and final approval. We build workable strategies from messy context to structured plans across markets and industries.

Learn more at: *************

The Role

We are seeking talents with 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

• 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.
• 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.