Senior Research Engineer (ML infrastructure )

We are looking for a Senior Research Engineer to build and improve the ML infrastructure that powers our deep learning research and trading. Our DL strategies are becoming an increasingly important driver of the business, and the infrastructure behind them has a direct impact on how quickly researchers can test ideas, how efficiently we use compute, and how reliably successful research reaches production across markets and asset classes. You will work at the boundary between deep learning research and infrastructure. Your job is not just to maintain ML systems, but to identify what is slowing research down, solve technically difficult problems across the stack, and turn research prototypes into reliable, scalable production systems. The scope is broad: training frameworks, datasets and data pipelines, distributed training, experiment infrastructure, reproducibility, observability, and tooling around the research lifecycle. Depending on your strengths, you may go deep in one of these areas or work across several of them. This is a highly hands-on IC role with substantial technical ownership. You will work closely with researchers, other research engineers, and infrastructure teams, and will be expected to independently drive important problems from diagnosis to production. Responsibilities Turn successful research prototypes into robust implementations that can be trained, validated, and deployed across multiple markets and asset classes; Work directly with researchers to remove infrastructure bottlenecks from the research loop — make new ideas easier to prototype, experiments faster to run, and failures easier to understand; Build and improve our internal ML platform — training framework, datasets and data pipelines, orchestration, experiment tracking and reproducibility tooling, GPU/compute infrastructure, and tooling for releasing models into production; Own technically challenging areas of the ML stack end-to-end: identify problems, design solutions, implement them, measure their impact, and maintain them in production; Make training reproducible and observable: investigate regressions, debug models that no longer reproduce from master, and improve tooling around data, experiments, model quality, and training behavior; Take ownership of shared training, dataset, and research infrastructure code — proactively find bugs, reduce technical debt, improve abstractions, and maintain a high bar through rigorous code review; Work across team boundaries when a research problem spans datasets, storage, compute, training infrastructure, or production systems; Identify areas where researchers are repeatedly paying an infrastructure tax and build reusable solutions instead of fixing the same problem case by case; When useful, contribute directly to research: run experiments, investigate model behavior, prototype architectural or optimization ideas, and help push model quality forward. Requirements A strong, versatile ML systems / research…