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IMC Trading is seeking engineers for Research Engineering to turn research ideas into scalable production pipelines. You'll build data ingestion, feature storage, validators, and experiment trackers while collaborating with researchers and traders.
You will work across languages (Python and C++) and own systems in production, ensuring robust deployment and fast feedback loops for continuous improvement.
The Research Engineering team develops and deploys the pipelines connecting a research idea all the way to its rollout in production. We exist to ensure that the entire pipeline describing our research is efficient, effective, and scalable. People in our team are avid collaborators and work in tight feedback loops with our researchers. We talk through problems on desk, prepare solutions same day, and work out how to deploy safely for tomorrow.
The scope of the team covers anything that accelerates the path from research to production at massive scale. This could be designing our external data ingestion pipelines, writing our feature discovery and storage systems, building our model validators and experiment trackers, authoring and deploying a bespoke algorithm, or figuring out how to roll out something novel.
We use a broad toolkit including Python, C++ and internal domain-specific languages, and are happy to pick up new tools and languages as required.
A finance background. Every language we touch. A particular set of years in any particular role. We hire engineers who like to solve the problems our traders face - we'll teach you the context on the ground.
IMC is a research-driven trading firm where quantitative modeling, machine learning, and engineering shape how modern markets are traded. A stabilizing force in markets since 1989, we provide liquidity across trading venues, delivering the best outcome in value and risk management to investors. Using our own technology and capital, we build proprietary systems and algorithms that operate across global markets. Our researchers, traders, and engineers work as a collective, combining rapid experimentation, advanced infrastructure, and real-time feedback to turn insight into execution and execution into advantage.