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AI Engineer - Foundation Models (Hong Kong)

Simudyne

Location TBDFull-timePosted 6mo agoStill listed 3 days ago

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At a glance

Compensation
No compensation found
Location
Location TBD
Schedule
Full-time
Work Authorization
Not specified

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Job overview

Simudyne is hiring an AI Engineer - Foundation Models (Hong Kong). The AI Engineer will develop and scale foundation models for financial applications, handling multi‑modal data and optimizing training on multi‑GPU/TPU clusters. Responsibilities include managing cloud compute, building inference pipelines, implementing distributed training, and collaborating with research teams to bring novel architectures into production.

Key focus areas include Train and fine-tune large‑scale foundation models across multiple modalities, Scale training on multi‑GPU/TPU clusters with efficient parallelization, and Manage compute resources and optimize training costs across cloud providers.

Important skills include Cloud Infrastructure, JAX, PyTorch, Distributed Training, Transformer Architectures, and Multi‑GPU/TPU Training.

Skills & qualifications

RequiredNice to have

Skills

Cloud InfrastructureJAXPyTorchDistributed TrainingTransformer ArchitecturesMulti‑GPU/TPU TrainingFine‑Tuning TechniquesMLOps Tools

Qualifications

Experience Training Models With 1B+ ParametersExperience With Multi‑Modal Data (Vision, Audio, Tabular, Time Series)Background in High Frequency Trading

Full job description

Careers Open Positions 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

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