AI Engineer – Foundation Models (Hong Kong)

Location:

Hong Kong

Key Responsibilities

• Train and fine-tune large-scale foundation models across multiple modalities (text, time series, vision, audio) for financial applications
• Scale training across multi-GPU/TPU clusters with efficient parallelization strategies
• Manage compute resources and optimize training costs across cloud providers (GCP, AWS, or similar)
• Build efficient inference pipelines for production deployment
• Implement distributed training strategies (data/model/pipeline parallelism)
• Develop custom JAX/PyTorch implementations for novel architectures
• Monitor and debug large-scale training runs with experiment tracking
• Collaborate with research teams to translate novel architectures into production-ready systems

Essential Requirements

• Experience training models with 1B+ parameters
• Strong expertise in JAX and/or PyTorch at scale
• Hands-on experience with multi-GPU/TPU training and optimization
• Deep understanding of transformer architectures and attention mechanisms
• Experience with distributed training frameworks (DeepSpeed, FSDP, Accelerate)
• Experience with foundation model fine-tuning techniques (LoRA, QLoRA, PEFT)
• Experience with cloud infrastructure for ML workloads (GCP, AWS, or similar)

Nice to Have

• Experience training models across multiple modalities (vision, audio, tabular, time series)
• Experience with diffusion model architectures
• Track record of publishing at top ML/AI conferences (NeurIPS, ICML, ICLR, or similar)
• Background in High Frequency Trading (HFT) or low-latency financial applications
• Experience with mixture-of-experts (MoE) architectures
• Knowledge of quantization and model compression techniques
• Familiarity with MLOps tools (Weights & Biases, MLflow)

What We Offer

• Work on cutting-edge AI for major financial institutions (LSEG, Barclays)
• Access to significant compute resources (GPUs/TPUs)
• Direct impact on core AI technology
• Competitive salary