What You'll Be Doing

Extend the proprietary distributed dataset API with new operators, optimizations, and improved user experience
Design and implement machine learning pipelines covering training, tuning, serving, validation, and retraining
Enhance reliability, observability, and multi-user features of compute infrastructure, including resource management and workload isolation
Develop durable abstractions to maintain platform flexibility with evolving distributed compute backends
Write, support, maintain, and test code using unit testing, documentation, automation, and CI/CD workflows
Collaborate on platform direction, design, and architecture with team members
Work closely with researchers and platform engineers to align compute and storage capabilities

Requirements

6+ years of Python and distributed systems experience in a quantitative finance environment
Bachelor’s degree in Computer Science, Mathematics, Financial Engineering, Operations Research, or related field
Deep knowledge of distributed compute frameworks such as Ray, Dask, Spark, PySpark, or Slurm
Hands-on experience with machine learning and deep learning workflows using PyTorch and XGBoost
Strong Python skills, optionally with kdb+ for platform and research development
Proficient with Git, Unix/Linux, Bash, and modern CI/CD workflows
Strong communication and collaboration skills in a team environment

Experience with kdb+, PyKX, Polars, Arrow, or columnar/lazy query engines
Knowledge of cash equities, live analytics, and cloud tooling including Kubernetes