Machine Learning Research Engineer - Tier 1 Global Hedge Fund

The RoleA leading systematic trading organization is seeking a Machine Learning Researcher to work on some of the most intellectually demanding problems in quantitative finance. This is a research-first role at a firm where ML is not a support function — it is core to how the organization generates alpha. You will work with exceptional data, exceptional colleagues, and the infrastructure to turn rigorous research into real impact.This firm sits in a small peer group globally. The problems are hard, the feedback loop is real, and the standard for research quality is unusually high. If you have spent your career working on ML at the frontier and want to apply it somewhere the results are measurable in the most direct way possible, this is a rare opening.What you'll doResearch & modelingDevelop and evaluate novel ML models applied to financial market data across the full research lifecycleDesign experiments with rigorous methodology - you are expected to be deeply skeptical of your own resultsIdentify signal in high-dimensional, noisy, non-stationary environments where standard approaches break downPush the boundaries of what is currently used in production - original thinking is expected and rewardedImplementation & productionOwn your research end-to-end, from initial hypothesis through to live deployment alongside the engineering teamWrite clean, well-tested, production-quality code - research that cannot be deployed has limited value hereCollaborate closely with quantitative researchers and engineers on model integration and performance monitoringMaintain and improve live systems as market conditions evolveCollaboration & cultureEngage openly with a small, senior research team - ideas are debated on their merits regardless of seniorityPresent research clearly and defend methodology under rigorous peer scrutinyContribute to a culture where intellectual honesty and reproducibility are non-negotiableMentor junior researchers as the team growsExploration & breadthStay at the frontier of ML research and evaluate applicability of emerging techniques to the firm's problem setWork across varied data types and market regimes - no two problems look the sameDevelop a personal research agenda with support, resources, and compute to pursue it seriouslyContribute to the firm's long-term thinking on where ML in markets is headingTechnical areas of interestDeep learning Time series modeling Reinforcement learning Probabilistic modeling NLP / alternative data Representation learning Python PyTorch / JAX Distributed computeWho you arePhD in machine learning, statistics, mathematics, physics, computer science, or a closely related field - or equivalent research output at an exceptional levelA track record of original ML research: published work, competition results, or demonstrated impact in a prior role that speaks for itselfDeep command of modern ML methodology - you understand not just how to run models, but why they work and where they will failFluency in Python and the scientific computing stack; comfort writing code that others can read, test, and build onExperience with or genuine intellectual interest in applying ML to financial markets, time series, or other high-noise sequential data domainsAn instinct for what makes a result real versus an artifact - you are more interested in durable findings than impressive backtestsComfortable operating with autonomy in an environment with limited structure and high expectationsWhy this roleThe best ML researchers in finance tend to cluster at a small number of organizations. This is one of them. The problems are real, the feedback is direct, and the culture treats research as a craft rather than a production function. You will not spend your time maintaining legacy systems or justifying headcount. You will spend it thinking, building, and finding out whether your ideas hold up.