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AI Engineer Intern - Foundation Models for Financial Applications (Hong Kong)

Simudyne

Location TBDFull-timePosted 6mo agoStill listed 4 days ago

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At a glance

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

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Job overview

Simudyne is hiring an AI Engineer Intern - Foundation Models for Financial Applications (Hong Kong). The AI Engineer Intern will assist in training and fine-tuning large foundation models for financial applications, support scaling across multi‑GPU/TPU clusters, build inference pipelines, and develop custom JAX/PyTorch implementations while monitoring training runs with experiment‑tracking tools.

Key focus areas include Assist in training and fine‑tuning large‑scale foundation models for financial applications, Support scaling of training runs across multi‑GPU/TPU clusters, and Contribute to building and optimizing inference pipelines for deployment.

Important skills include Deep Learning, High-Frequency Trading, Cloud Platform System, High Frequency Trading, JAX, and PyTorch.

Skills & qualifications

RequiredNice to have

Skills

Deep LearningHigh-Frequency TradingCloud Platform SystemJAXPyTorchMulti GPU TrainingTransformer ArchitecturesAttention MechanismsDistributed Training FrameworksLoRAQLoRAPEFTDiffusion ModelsAutoregressive ArchitecturesGCPAWSMLOps ToolsWeights & BiasesMLflowMixture of ExpertsQuantizationModel Compression

Full job description

Careers Open Positions AI Engineer Intern – Foundation Models for Financial Applications (Hong Kong) Location

Hong Kong

Key Responsibilities

  • Assist in training and fine-tuning large-scale foundation models for financial applications

  • Support scaling of training runs across multi-GPU/TPU clusters

  • Contribute to building and optimizing inference pipelines for deployment

  • Help implement and experiment with distributed training strategies

  • Develop and test custom JAX/PyTorch implementations for novel architectures

  • Assist in monitoring and debugging training runs using experiment tracking tools Essential Requirements

  • Exposure to training or fine-tuning deep learning models (experience with 100M+ parameter models is a plus)

  • Familiarity with JAX and/or PyTorch

  • Basic understanding of multi-GPU training concepts

  • Familiarity with transformer architectures and attention mechanisms

  • Exposure to distributed training frameworks (DeepSpeed, FSDP, Accelerate, or similar)

  • Familiarity with foundation model fine-tuning techniques (LoRA, QLoRA, PEFT) Nice to Have

  • Background in High Frequency Trading (HFT) or financial applications

  • Exposure to diffusion models or autoregressive architectures

  • Familiarity with cloud platforms for ML workloads (GCP, AWS, or similar)

  • Familiarity with MLOps tools (Weights & Biases, MLflow)

  • Any coursework or projects touching on mixture-of-experts, quantization, or model compression What We Offer

  • Hands-on experience working on cutting-edge AI for major financial institutions (LSEG, Barclays)

  • Access to significant compute resources (GPUs/TPUs)

  • Competitive internship stipend

  • Mentorship from senior AI engineers with direct impact on core technology

  • Potential for full-time conversion upon successful completion

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