Senior/Staff AI Model Engineer

. Own the reliability and quality bar for an AI copilot embedded in a trading platform used by sophisticated investors .

Posted 9/25/2026full-timeRemote • United StatesSenior💰 $200,000 - $350,000 per yearWebsite

Tools & technologies

PostgresReactReact NativeRustTypeScript

Key responsibilities & impact

Own the reliability and quality bar for an AI copilot embedded in a trading platform used by sophisticated investors
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 including curated golden sets, scenario suites, stress/adversarial cases, and 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 with deterministic previews, confirmations, and auditability
Own model improvement loops tied to evaluations, including data collection/labeling strategies, error taxonomy, prompt/tooling changes, and appropriate fine-tuning or preference optimization
Design and operate AI monitoring and incident response, including telemetry, alerting, root-cause analysis, and fix-forward processes
Develop deep understanding of trading concepts such as margin, shorting, portfolio margin, risk, and execution, and express them accurately to users
Work with the technology stack: Rust, TypeScript, Postgres, React, React Native, observability/telemetry tooling, LLM APIs, model serving, and evaluation/training pipelines

What you’ll need

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
Bonus: Experience with fine-tuning, preference optimization, distillation, or prompt/compiler-style techniques for improving tool-use reliability
Bonus: Experience creating domain-specific benchmarks and adversarial suites for high-stakes applications
Bonus: Deep experience with trading across asset classes, margin types, etc.
Bonus: Experience with Rust and performance-sensitive services
Bonus: Experience designing incident response and SLOs for ML/AI systems

Comp & perks