Job Function
Financial Services
Description
Responsibilities- Lead the design and development of a scalable, reliable, and reproducible machine learning research platform.
- Build infrastructure to support large-scale experimentation, model training, and simulation across both on‑premise high‑performance compute environments and multi‑cloud setups.
- Work closely with researchers to understand evolving workflows and translate those needs into robust platform capabilities.
- Architect and optimize distributed training pipelines for high-throughput, GPU‑accelerated workloads.
- Enhance experiment management, model versioning, artifact tracking, and data lineage to ensure transparent and repeatable research processes.
- Develop tools and frameworks that improve feature engineering, dataset creation, and large-scale backtesting.
- Drive initiatives to improve compute efficiency, resource allocation, and workload isolation across heterogeneous environments.
- Enhance platform observability with improved metrics, logging, tracing, and debugging capabilities tailored to ML and distributed systems.
- Support rapid iteration by delivering features and fixes quickly while maintaining strong engineering standards.
- Contribute to long-term architectural planning to ensure the platform scales with growing data volumes and model complexity.
Qualifications
- 2+ years of experience designing and building distributed systems at scale, ideally supporting research or data-heavy workloads.
- Strong programming skills in Python with a focus on clean, maintainable, high-performance code.
- Experience running applications on Linux-based HPC clusters and/or cloud computing platforms.
- Solid understanding of distributed computing, parallel processing, and resource management.
- Hands-on experience with GPU workloads and familiarity with modern ML frameworks such as PyTorch, TensorFlow, or JAX.
- Experience optimizing data pipelines and handling large structured and unstructured datasets.
- Strong debugging skills with the ability to diagnose issues across multiple layers of the stack.
- Comfortable working independently in a fast-paced, research-oriented environment.
- Strong communication skills and experience collaborating directly with researchers or data-focused teams.
Preferred Attributes
- Experience building internal ML platforms or research tooling at scale.
- Familiarity with experiment‑tracking tools, workflow orchestration systems, and model lifecycle management.
- Experience with containerization and orchestration technologies (e.g., Docker, Kubernetes).
- Exposure to high-performance or latency-sensitive domains such as quantitative research, simulation systems, or large‑scale distributed compute.
Job ID: 82981908
