Financial Foundation Model Architect

Didi-Global-; San Jose (CA); On-site; USD 180,000 - 260,000; Full time

Job summary

DiDi Global Inc. is a leading mobility technology platform seeking a senior ML researcher to drive foundation model pre-training pipelines and multi-domain sequence modeling for financial risk applications.

You will collaborate with product and engineering teams to ship high-impact models, optimize training across large-scale data, and deploy models in production with robust evaluation and monitoring.

Qualifications

• Must have: Master's degree or above in Computer Science, Mathematics, Statistics, or a related field.
• 3+ years of deep learning algorithm R&D experience with hands-on experience building a pre-trained model from scratch and completing the full training pipeline.
• Proficiency in Transformer architectures and variants (GPT/BERT/FT-Transformer/MoE).
• Familiar with PyTorch and distributed training (multi-GPU / multi-node).
• Strong experimental design skills including ablation studies and scaling-law experiments.

Responsibilities

• Foundation Model Pre-training: design and implement pre-training pipeline for financial behavior sequence models and experiments.
• Multi-source Sequence Modeling: build unified sequences across payments, ride-hailing, food delivery, and credit data.
• Ablation Studies and Evaluation Framework: create a systematic ablation framework and evaluation pipeline.
• Production Deployment: integrate pre-trained representations into downstream risk-control scenarios and deploy online.

Skills

Deep learning

Transformer architectures

PyTorch

Distributed training

Sequence modeling

Education

Master's degree or above in Computer Science, Mathematics, Statistics, or a related field

Tools

PyTorch

Multi-GPU / multi-node

Job description

DiDi Global Inc. is a leading mobility technology platform seeking a senior ML researcher to drive foundation model pre-training pipelines and multi-domain sequence modeling for financial risk applications.

You will collaborate with product and engineering teams to ship high-impact models, optimize training across large-scale data, and deploy models in production with robust evaluation and monitoring.