SDET/QA Engineer

SDET/QA Engineer

Downtown Chicago – Onsite

About The Role:

Seeking a technically strong SDET to ensure quality across trading systems, order execution, real-time risk analytics, and AI-driven models.

What You’ll Do:

• Develop end-to-end, integration, and unit tests for options pricing, order lifecycle, and P&L.
• Validate AI/ML models, including signal generation, forecasting, regression, and drift detection.
• Test LLM-powered tools for accuracy, latency, and hallucination detection.
• Simulate volatile market events, expirations, and corporate actions for system validation.
• Verify FIX protocol messaging, OMS/EMS integrations, and exchange connectivity.
• Partner with quants to validate Greeks and pricing model accuracy.
• Build performance/load tests to meet sub-millisecond latency requirements.
• Ensure data quality across training data, feature stores, and model inputs.
• Support code reviews and improve testability across applications.
• Manage CI/CD quality gates, including AI/ML model deployment validation.
• Investigate production issues and create regression tests to prevent recurrence.

What We’re Looking For:

• 4+ years of SDET/QA experience, including 2+ years in financial services or trading systems.
• Proficient in Python and/or Java/C++ test automation.
• Strong knowledge of options trading, Greeks, and pricing models.
• Experience testing real-time, event-driven systems (Kafka, FIX, WebSockets).
• Hands-on with pytest, JUnit, TestNG, or similar frameworks.
• Understanding of AI/ML concepts, model validation, and evaluation metrics.
• Experience testing end-to-end ML pipelines and data workflows.
• Strong SQL, algorithms, data structures, and distributed systems knowledge.
• Familiarity with time-series databases (kdb+, InfluxDB, TimescaleDB).
• Experience with CI/CD tools (Jenkins, GitLab CI, GitHub Actions).
• Ability to validate quantitative models and pricing logic.
• Experience evaluating LLMs and AI model performance.
• Familiarity with ML lifecycle tools (MLflow, SageMaker, Weights & Biases).
• Knowledge of AI governance, model risk, and financial regulations (FINRA, SEC, CFTC).
• Experience with market simulators, exchange emulators, or low-latency trading environments.
• Exposure to FPGA/co-location infrastructure, chaos testing, or kdb+/q is a plus.
• Open-source testing or ML evaluation contributions preferred.

OOJ-1483G