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Selby Jennings Linkedin · Posted 1mo ago

Machine Learning Engineer

Shanghai

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Indexed description

Responsibilities

  • Architecting and developing the next generation of Company's machine learning research platform, with an emphasis on scalability, reliability, observability, and reproducibility
  • Building infrastructure that enables large-scale experimentation, model training, and simulation across on-premises HPC and multi-cloud environments
  • Partnering closely with quantitative researchers to understand evolving research workflows and translate them into robust platform capabilities
  • Designing and optimizing distributed training pipelines for high-throughput, GPU-accelerated workloads
  • Improving experiment management, model versioning, artifact tracking, and data lineage to ensure transparent and reproducible research
  • Developing tools and frameworks that streamline feature engineering, dataset generation, and large-scale backtesting
  • Leading initiatives to improve compute efficiency, resource scheduling, and workload isolation across heterogeneous environments
  • Enhancing platform observability, including metrics, logging, tracing, and debugging capabilities tailored to ML workloads
  • Supporting rapid iteration by implementing features and fixes on tight timelines while maintaining high engineering standards
  • Contributing to long-term architectural decisions that enable the platform to scale with increasing data volumes and model complexity

Qualifications

  • 2+ years of experience designing and building large-scale distributed systems, ideally in support of research or data-intensive workloads
  • Strong programming experience in Python, with a focus on writing clean, maintainable, and high-performance code
  • Experience developing and operating applications on Linux-based HPC clusters and/or cloud platforms
  • Solid understanding of distributed computing concepts, parallel processing, and resource management
  • Experience with GPU-based workloads and familiarity with modern ML frameworks (e.g., PyTorch, TensorFlow, JAX)
  • Experience optimizing data pipelines and handling large-scale structured and unstructured datasets
  • Strong troubleshooting skills with the ability to debug complex, cross-layer system issues
  • Ability to work independently in a fast-paced, research-driven environment
  • Strong communication skills and experience collaborating directly with researchers or data scientists
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