Note: The job is a remote job and is open to candidates in USA. Clear Street is modernizing the brokerage ecosystem by building a cloud-native clearing and custody system for today's complex markets. The Senior / Staff AI Model Engineer will be responsible for ensuring the reliability and quality of an AI copilot in a trading platform, developing evaluation systems, and improving model performance through various methodologies.
Responsibilities
- 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
Skills
- At least Eight (8) years of experience shipping production software; strong proficiency with 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
- 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 (e.g., “known-bad” scenarios) for high-stakes applications
- Deep experience with trading across asset classes, margin types, etc
- Experience with Rust and performance-sensitive services
- Experience designing incident response and SLOs for ML/AI systems
Benefits
- Company equity
- 401k matching
- Gender neutral parental leave
- Full medical, dental and vision insurance
- Lunch stipends
- Fully stocked kitchens
- Happy hours
- A great location
- Amazing views
Company Overview
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