Responsibilities
Qualifications
- 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
Référence PR/595853
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