The posting, in Clear Street's own words
archived Sep 28, 2026Clear Street is modernizing the brokerage ecosystem. Founded in 2018, Clear Street is a diversified financial services firm replacing the legacy infrastructure used across capital markets. We started from scratch by building a completely cloud-native clearing and custody system designed for today's complex, global market. Our platform is fully integrated with central clearing houses and exchanges to support billions in trading volume per day. We've agonized about our data model abstractions, created horizontal scalability, and crafted thoughtful APIs. All so we can provide a best-in-class experience for our clients. By combining highly-skilled product and engineering talent with seasoned finance professionals, we're building the essentials to compete in today's fast-paced markets.
The Team:
The mission of the Clear Street Active team is to provide best execution for every asset class in every market. Active is currently building a new, state-of-the-art, cloud-based trading platform providing high-performance traders access to liquidity venues across multiple asset classes, cutting-edge charting capabilities, and sophisticated order handling with the flexibility to service both the active trader and institutional workflows.
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The Role:
You will own the reliability and quality bar for an AI copilot embedded in an trading platform used by sophisticated investors You will design and build evaluation systems that measure correctness, safety, latency, and regression risk across market analysis, portfolio/risk reasoning, and trading workflows (including order placement) You will develop and maintain benchmarks: curated “golden sets,” scenario suites, stress/adversarial cases, and continuously refreshed market/regime-based test corpora You will build automated quality gates and regression workflows that block releases when key metrics degrade You will partner with engineering and product to define safe tool/action contracts (deterministic previews, confirmations, auditability) and ensure predictable assistant behavior You will own model improvement loops tied to evals: data collection/labeling strategies, error taxonomy, prompt/tooling changes, and when appropriate, fine-tuning or preference optimization to measurably improve benchmark performance You will design and operate monitoring + incident response for AI: telemetry, alerting, RCA, and “fix-forward” processes You will develop a deep understanding of trading concepts (margin, shorting, portfolio margin, risk, execution) and how to express them accurately and understandably to users Tech Stack: Rust, TypeScript, Postgres, React (Web), React Native (Mobile), observability/telemetry tooling, APIs + model serving, eval/training pipelines