Responsibilities - Architecting and developing the next generation of our 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 Preferred Attributes - Experience building internal ML platforms or research tooling at scale - Familiarity with experiment tracking systems, workflow orchestration frameworks, and model lifecycle management - Experience with containerization and orchestration technologies (e.g., Docker, Kubernetes) - Exposure to quantitative finance, simulation systems, or other latency- and performance-sensitive domains