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Salary: USD 200,000 - 350,000 / annual
Clear Street is modernizing the brokerage ecosystem with a cloud-native clearing, custody, and trading platform. The Active team is building a state-of-the-art trading platform providing high-performance traders access to liquidity venues across multiple asset classes with cutting-edge charting and sophisticated order handling.
You will own the reliability and quality bar for an AI copilot embedded in the trading platform used by sophisticated investors. Your core responsibilities include:
- Design and build evaluation systems measuring correctness, safety, latency, and regression risk across market analysis, portfolio/risk reasoning, and trading workflows (including order placement)
- Develop and maintain benchmarks: curated "golden sets," scenario suites, stress/adversarial cases, and continuously refreshed market/regime-based test corpora
- Build automated quality gates and regression workflows that block releases when key metrics degrade
- Partner with engineering and product to define safe tool/action contracts (deterministic previews, confirmations, auditability) and ensure predictable assistant behavior
- Own model improvement loops tied to evals: data collection/labeling strategies, error taxonomy, prompt/tooling changes, and when appropriate, fine-tuning or preference optimization
- Design and operate monitoring and incident response for AI: telemetry, alerting, RCA, and "fix-forward" processes
- Develop deep understanding of trading concepts (margin, shorting, portfolio margin, risk, execution) and express them accurately to users
Tech stack includes Rust, TypeScript, Postgres, React (Web), React Native (Mobile), observability/telemetry tooling, LLM APIs, model serving, and eval/training pipelines.
**Requirements:**
- At least 8 years of experience shipping production software with strong proficiency in any programming language
- Strong knowledge of computer science fundamentals, testing methodology, and systems design
- Experience building evaluation frameworks, test harnesses, and benchmark suites for complex systems (LLMs, agents, search, retrieval, ranking, recommenders)
- Experience running model improvement cycles: dataset curation, labeling/QA, offline experimentation, and deploying changes with measurable impact on benchmarks
- Ability to define metrics, build measurement pipelines, and drive engineering/product decisions from data
- Comfort working across the stack: debugging model/tooling failures, instrumenting services, and partnering with frontend/product on UX patterns that improve safety and trust
- High degree of self-motivation and willingness to jump into unfamiliar areas to solve problems
**Bonus qualifications:**
- Experience with fine-tuning, preference optimization, distillation, or prompt/compiler-style techniques for improving tool-use reliability
- Experience creating domain-specific benchmarks and adversarial suites for high-stakes applications
- Deep experience with trading across asset classes and margin types
- Experience with Rust and performance-sensitive services
- Experience designing incident response and SLOs for ML/AI systems
