Top Engineering Jobs in Chicago, IL

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.

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.

Designs, implements, maintains, and optimizes North American networking infrastructure connecting trading operations, data centers, exchanges, and global offices. Responsibilities include network architecture, engineering projects, performance optimization, monitoring, incident resolution, documentation, reporting, and support for UNIX, Windows, SAN, and data center deployments. The role also requires troubleshooting advanced network technologies and mentoring junior network team members.

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.

Design and verify next-generation ASICs for latency-sensitive trading systems. Responsibilities include RTL development in SystemVerilog, verification and testing, debugging, integration, tooling and automation, and cross-team collaboration to meet performance and reliability goals.

Design, implement, and deploy ML inference engines on custom FPGA/ASIC hardware. Perform HW/SW co-design with traders and researchers, optimize neural networks for low latency, and build quantization/compression tools to translate ML frameworks to RTL for rapid production deployment.

This role involves ensuring system continuity in trading operations, developing automation tools, and improving system performance in a trading environment.

Support researchers and ML engineers to optimize and scale HPC workloads on CPU/GPU infrastructure. Analyze performance, diagnose bottlenecks across storage, networking, and schedulers, implement profiling and benchmarking, influence platform design, and define best practices for shared compute environments while ensuring security and compliance.