We are looking for a Senior Research Engineer to join a premier Systematic Technology team. In this role, you will be the core engineering force enabling Quantitative Researchers to iterate fast and scale seamlessly. You will design, build, and optimize Python-based research tooling and data infrastructure capable of processing massive volumes of financial and alternative datasets.
If you thrive at the intersection of high-performance Python engineering, large-scale data curation, MLOps, and compute optimization—and take pride in clean, reliable data pipelines—this is a high-impact position designed for you.
Key Responsibilities
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Research Infrastructure & Tooling: Design, build, and scale core Python frameworks and internal libraries used directly by Quantitative Researchers.
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Orchestration & Workflow Pipelines: Develop robust orchestration systems for large-scale data analysis, automated feature generation, and complex research workflows.
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Data Curation & Management: Curate and manage broad financial and alternative datasets, enforcing rigorous standards around data quality, consistency, point-in-time accuracy, and usability.
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Pipeline Engineering: Continually improve ingestion, validation, transformation, and distribution pipelines to ensure zero-data-loss and low-latency delivery.
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Quantitative Collaboration: Partner closely with Quant Researchers to translate sophisticated quantitative models and research needs into scalable, production-grade engineering solutions.
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MLOps & Experiment Management: Support MLOps workflows, ensuring full experiment reproducibility, automated pipeline tracking, and streamlined model management.
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GPU & Compute Optimization: Optimize GPU resource allocation and utilization across compute-heavy research workloads and parallelized machine learning jobs.
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Platform Scalability: Work alongside platform and infrastructure engineering teams to guarantee high availability, scalability, and system efficiency.
Required Qualifications
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Python Expertise: Excellent software engineering foundation in modern Python (clean code, modular design, performance tuning).
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Data & Research Infrastructure: Direct experience building data-intensive platforms, research tooling, or large-scale data pipelines.
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Financial & Alternative Datasets: Solid hands-on experience working with large-scale financial markets data, alt-data, or high-throughput tabular time-series.
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Data Quality & Curation: Uncompromising attention to detail and rigorous practices around data validation, cleansing, and curation.
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Orchestration & Scale: Proven experience with data orchestration frameworks and large-scale analytical processing.
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MLOps Foundations: Practical familiarity with MLOps concepts, experiment tracking, and pipeline automation.
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GPU Compute: Experience supporting or accelerating GPU-based research workloads or machine learning infrastructure.
Preferred Qualifications
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Background working in systematic trading, quantitative finance, hedge funds, or high-frequency trading environments.
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Hands-on experience with distributed computing systems (e.g., Ray, Spark, Dask) and reproducible research environments.
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Familiarity with cloud platforms (AWS/GCP/Azure), containerization (Docker, Kubernetes), and shared compute clusters.
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