Jobs in Japan
Explore hand-picked jobs in Japan for English speakers across tech, education, marketing, and more.
Vetted companies only. Apply from overseas.
Jobs in Japan
Explore hand-picked jobs in Japan for English speakers across tech, education, marketing, and more.
Vetted companies only. Apply from overseas.
MercariMerpay Credit Modeling team's mission is to "create a credit business that delivers value to everyone using high-quality data science and machine learning technology." Under this mission, we handle everything from identifying business challenges to model construction, development, and operations. Currently, the team is working on developing a next-generation credit modeling platform using deep learning. Leveraging diverse data from the Mercari ecosystem, we aim to build versatile foundation models that can be applied to multiple tasks across credit and other domains. In this position, as a core member responsible for the large-scale deep learning models at the heart of our next-generation credit system, you will play a central role in everything from evaluating model architectures and training strategies to production deployment and continuous improvement. Design, develop, and improve core large-scale deep learning models for next-generation credit modeling, including model architecture design based on the challenges and data characteristics of the credit domain; training task and objective-function design and scaling policies considering data, model architecture, and compute budgets; GPU-based large-scale and distributed training infrastructure; and evaluation encompassing offline metrics, business KPIs, risk metrics, stability, fairness, and explainability. Deploy and operate large-scale deep learning models in production by designing training and inference pipelines, optimizing inference efficiency, integrating models into production systems, and designing monitoring, retraining, rollback, and MLOps practices. Make technical decisions about modeling policies and trade-offs such as model scale and performance versus cost, inference latency, interpretability, and coexistence with legacy models. Keep up with the latest research trends and evaluate their application to in-house challenges. Operate and continuously improve existing credit models. Lead consensus-building and collaboration with stakeholders in product, business, legal, and compliance. Provide technical mentorship in deep learning to team members and systematize knowledge. This role offers the opportunity to build next-generation financial deep learning models from scratch using Mercari's massive and diverse dataset, apply state-of-the-art deep learning and large-scale training to credit scoring and risk evaluation, deliver high-stakes production systems balancing accuracy, stability, explainability, and operational quality, and contribute to financial inclusion. The project is in an active development phase, offering significant autonomy in model architecture and training strategy decisions, with opportunities to grow into project leadership.
- Align with the mission and values of the Mercari Group and its companies. - 3+ years of experience in deep learning model development using frameworks such as PyTorch. - 1+ year of hands-on experience in pre-training 1B+ parameter Transformer-based models, LLMs, or foundation models from scratch. - Proven experience independently leading model training, experiment design, and result analysis. - 1+ year of practical MLOps experience deploying and operating deep learning models in production environments, or equivalent experience. - Strong communication skills to collaborate smoothly with team members and cross-functional stakeholders. - Japanese: Proficient (CEFR - C1).
- Experience leading the design of data architectures, model architectures, objective functions, training recipes, and compute budgets for pre-training. - Experience adapting and evaluating pre-trained models for downstream tasks, such as fine-tuning or transfer learning. - Experience with training acceleration, optimization, and tuning, such as distributed training. - Experience optimizing and accelerating inference, such as model compression techniques including knowledge distillation, quantization, pruning, or KV caching. - Experience in tabular data analysis and modeling, using deep learning or other approaches. - Experience in data analysis and system development in cloud environments such as AWS or Google Cloud. - Experience as a team or project lead, and experience mentoring junior engineers. - Experience working in financial institutions or Fintech companies. - English: Independent (CEFR - B2) Preferred.
