Responsibilities ● Architecting and developing the next generation of the 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 Requirements ● Bachelor's degree or higher from a reputable university in Computer Science, Engineering, or a related field ● 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 Qualifications ● 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