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Own global hardware sourcing strategy across GPUs, servers, networking, storage, and enterprise equipment. Manage executive relationships with strategic vendors representing $300M-$500M+ in annual spend, negotiate complex supply agreements, secure constrained GPU and component allocations, optimize costs and total cost of ownership, and mitigate supply-chain risks. Partner with engineering, finance, and capacity planning leaders on infrastructure roadmaps and investment decisions while coaching sourcing teams globally.

Conduct quantitative research to design, implement, and deploy mathematical trading models. Build custom tools for exploratory analysis, apply rigorous quantitative techniques to solve complex problems, and collaborate with traders and developers to productionize models while maintaining best practices.

Develop and maintain backend systems, automation, databases, data pipelines, dashboards, and visualization tools supporting finance, trading, and operations teams. Improve processing efficiency, system performance, and data accessibility across a global business intelligence environment. Collaborate with BI developers and business stakeholders while working with large datasets. The role requires Python, SQL, cloud, containerization, CI/CD, and agentic coding tool experience.

Interest form for students who want to learn more about the 2027 US Chess Academy. Applicants join a prospect list and may receive information about upcoming opportunities, program updates, and how to play.

Build, operate, and scale large-scale data storage platforms (Hadoop, S3-compatible, enterprise storage). Tune Linux for I/O performance, automate deployments with Ansible/Puppet, implement observability with Prometheus, and partner with engineers on storage- and Kafka-focused data solutions. Familiarity with Kubernetes and development in Java/Python required.

Performance Engineer Interns conduct quantitative research on exchange and network technologies, design optimization strategies for electronic trading, develop AI agents, ETL pipelines, and Python algorithms, and reverse-engineer and stress-test low-latency systems. They collaborate with traders and hardware and software engineers to improve liquidity-taking, quoting, and ultra-low-latency trading strategies.

Conduct quantitative research on exchange technology and large datasets, design strategies for ultra-low-latency trading, and develop AI agents, ETL pipelines, and Python algorithms. Reverse-engineer and stress-test network and systems technologies, collaborating with traders and hardware and software engineers to optimize liquidity-taking and quoting strategies.

The Quantitative Developer will develop systems from research to production, build simulation infrastructure, manage feature pipelines, and troubleshoot issues in quantitative trading environments.

As a Software Engineer, you will build AI systems to enhance developer workflows, implementing agents and servers, and ensuring code reliability through evaluation pipelines.

Design, build, and operate big-data and streaming platforms (Kafka, Hadoop, Dremio). Develop, deploy, and monitor data pipelines using Java, Python, Spark/Flink; collaborate on data modeling and ingestion, ensure data integrity and availability, act as a Big Data SME, and perform root-cause analysis.