About the position
Background of the recruitment and description of the project We are looking for new team members to support the expansion of our simulation business and the development of new services. Work content and job description [About Us] MQue provides solutions to clients across a wide range of industries, leveraging technologies such as fluid simulation and AI. [About the role] You'll develop AI surrogate models to accelerate CFD and AI agents to streamline and automate CFD workflows, with a focus on thermal fluids and gas-liquid two-phase flow. Working with our fluid engineers, you'll apply these technologies to real-world client challenges and evaluate their accuracy and efficiency. The role combines R&D and technical consulting. [Key Client Industries] Home appliance, and cooling equipment manufacturers; Data center companies; and Aerospace companies. [AI surrogate model R&D] - Designing, implementing, training, and evaluating models, and working with fluid engineers to design and prepare the datasets - Designing evaluation metrics and validation cases, and assessing prediction accuracy, runtime, generalization, and limits of applicability [AI agent development] - Connecting large language models to working tools to speed up information gathering, document preparation, and data organization - Implementing workflows that automate CFD preprocessing, case setup, execution, and postprocessing - Integrating with CFD solvers and analysis tools, logging execution history, and building failure detection and retry - Working with practitioners and domain experts to evaluate and improve time savings, accuracy, and reproducibility [Technical consulting] - Working with customers to define the problem, the requirements, and the criteria for success - Applying and validating prototypes built on AI surrogate models and AI agents - Explaining results and their limits, and hardening the software so customers can keep using it The above responsibilities are examples and may include other related tasks. Work experience What we're looking for; [Requirements] - PhD in a relevant field — computer science, machine learning, computational science, or similar - Experience designing, implementing, training, and evaluating machine learning models - Model development with PyTorch, JAX, or similar, and experience training and computing on GPUs - An interest in fluid phenomena and CFD, plus a working grasp of the fundamentals — for instance, how boundary conditions and numerical settings shape the results - The ability to frame technical problems with experts and customers, and explain your development approach, evaluation results, and limits of applicability - Conversational Japanese and English, or the willingness to build them up [Nice to have] - Experience building AI surrogate models, or applying machine learning to scientific and engineering computation - Expertise in fluid dynamics and numerical analysis, hands-on CFD experience, or experience analyzing experimental fluid data - Experience using AI agents to streamline work, automating CFD, or building and evaluating workflows that chain multiple tools together - Experience with data assimilation, inverse problems, design optimization, or differentiable simulation - MLOps and LLMOps, data pipelines, or experience turning research code into software that ships and runs - Experience with customer discovery, requirements definition, technical proposals, or presenting development results [The kind of person who does well here] - Someone who wants to deepen their understanding of fluid phenomena and analysis methods, and can turn discussions with specialists into implementation and evaluation - Someone who investigates the conditions under which AI models and agents fail, and drives the improvements real-world use demands - Someone who bases development decisions on evidence from validation, and can explain the reasoning and the limits to everyone involved Description [The environment behind the work] Every engineer in MQue's simulation business holds a PhD. We each bring a different specialty to the table, and we apply them both to our own research and to our customers' technical problems. Alongside our fluid specialists, you'll work with an expert in human-computer interaction. That means you get to design how AI actually gets used, from interactions and information display that fit the user's workflow to the review and judgment steps where a person stays in the loop. Professor Takehiro Himeno of the University of Tokyo, a leading figure in fluid simulation and gas-liquid two-phase flow, serves as an advisor. We run CPU and GPU clusters in house for R&D, and we support publishing papers and attending conferences. Supplementary explanation of compensation [Development Environment] - Company-provided PC of your choice - Access to in-house CPU and GPU clusters - Access to supercomputing and cloud computing resources - Access to AI development tools (e.g., Claude Code, Codex, Cursor) [Benefits] - Generous support for purchasing research papers and books - Support for research publications and conference participation - Company housing program - Employee stock ownership plan Considerations for conducting interviews (e.g., those who live far away, such as overseas) Interviews can be conducted online, and candidates based overseas or outside the Tokyo area are welcome to apply. Selection 1. Introductory Interview 2. Technical Assessment 3. Interview with Team Members 4. Final Interview with the CEO Notification of result Candidates will be notified of the outcome by email or phone.
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