We're looking for a sharp, technically deep SDET to own quality across our trading systems — from order execution engines to real-time risk analytics. You'll work alongside quants, traders, and engineers to ensure our platform behaves correctly under normal conditions and at the edge cases that matter most in live markets. As AI becomes core to how we trade and operate, you'll also help us test and validate the models and pipelines that power it.
WHAT YOU WILL DODevelop end-to-end, integration, and unit test suites covering options pricing models (Black-Scholes, binomial trees), order lifecycle, and P&L calculations
Test and validate AI/ML models used in signal generation, volatility forecasting, and trade execution — including drift detection, model regression, and output boundary testing
Build evaluation harnesses for LLM-powered tools used internally (e.g., trade summarization, risk Q&A, alert triage) to assess accuracy, hallucination rates, and latency
Simulate realistic market scenarios including high-volatility events, expiry dates, and corporate actions to stress-test system behavior
Validate FIX protocol messaging, OMS/EMS integrations, and exchange connectivity (CBOE, ISE, etc.)
Collaborate with quants to write test cases that verify Greeks (delta, gamma, vega, theta) and pricing accuracy under various market conditions
Build performance and load testing harnesses to validate sub-millisecond latency requirements
Design data quality pipelines to validate training data, feature stores, and model inputs for correctness and consistency
Participate in code reviews and advocate for testability in system design
Own CI/CD pipeline quality gates, including ML model promotion gates (shadow mode, A/B, champion/challenger)
Investigate production incidents and translate findings into regression tests
WHAT SPIDERROCK IS LOOKING FOR4+ years of SDET or QA Engineering experience, with at least 2 years in financial services or trading systems
Proficiency in Python and/or Java/C++ for test automation
Strong understanding of options trading concepts — calls/puts, expiry, strike, Greeks, volatility surfaces
Hands-on with test frameworks: pytest, JUnit, TestNG, or equivalent
Familiarity with ML concepts — model training, inference, overfitting, feature importance, and evaluation metrics (precision, recall, AUC)
Solid fundamentals in data structures, algorithms, and distributed systems
Familiarity with SQL and time-series databases (kdb+, InfluxDB, TimescaleDB)
Experience with CI/CD tools (Jenkins, GitLab CI, GitHub Actions)
Ability to read and reason about quantitative models and pricing logic
Experience writing LLM evaluation frameworks — prompt regression testing, output scoring, and consistency checks across model versions
Familiarity with MLflow, Weights & Biases, or SageMaker for model lifecycle tracking and test integration
Knowledge of AI governance and model risk management frameworks (SR 11-7 or equivalent) relevant to financial institutions
Experience with market simulators or exchange emulators
Knowledge of regulatory requirements (FINRA, SEC, CFTC) and audit trail testing including AI-assisted decision logging
Exposure to co-location or FPGA-based trading infrastructure
Familiarity with chaos engineering and fault injection testing
Prior experience with kdb+/q for tick data validation
Contributions to open-source testing or ML evaluation tools
WHAT TO EXPECT
SpiderRock is an Equal Opportunity Employer
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