Production ML Engineer — Risk & AI for Crypto

OKX; San Jose (CA); On-site; USD 214,666 - 321,999; Full time

Job summary

OKX in San Jose is hiring a machine learning engineer for risk. You will design, build, and deploy models for payment fraud, account takeover, and other risk signals across a global crypto platform.

You will own end-to-end production ML systems, collaborate with risk strategy, and develop AI-assisted workflows including LLM-based agents, with a focus on safety, explainability, and scalable performance.

Qualifications

• Significant professional experience in ML engineering or applied data science.
• Strong Python skills and experience with ML frameworks such as PyTorch, TensorFlow, XGBoost, LightGBM, or scikit-learn.
• Knowledge of supervised learning, anomaly detection, representation learning, class-imbalanced modeling, model calibration.
• Fluency with AI-assisted engineering; experience with AI-integrated workflows and LLM coding tools.
• Familiarity with SHAP, feature attribution, reason-code generation, and model scorecards.
• Experience deploying production LLM agents and tool calling, retrieval-augmented generation, prompt and context management.

Responsibilities

• Design, build, and deploy ML models for risk use cases such as payment fraud, account takeover, scam detection, deposit and withdrawal risk, promotional abuse, customer risk assessment, and transaction monitoring.
• Own production ML systems end to end, including feature pipelines, training workflows, model serving, decision integrations, monitoring, alerting, drift detection, retraining, and incident response.
• Partner with risk strategy and product teams to translate models into effective production controls, including approval, rejection, review, cooldown, limit adjustment, account restriction, and other risk mitigation actions.
• Work closely with risk operations teams to understand investigation workflows, incorporate reviewer feedback, improve model explainability, and continuously refine labels and training data.
• Apply AI-assisted development throughout the engineering workflow, using LLM coding tools to accelerate implementation, testing, debugging, analysis, and documentation while maintaining appropriate security and review standards.
• Develop AI-powered risk capabilities such as investigation agents, case summarization, evidence collection, review recommendations, alert triage, suspicious-entity mining, and automated decision support.
• Take research-stage models into reliable production systems by validating feature logic, reviewing data quality, addressing latency and scalability constraints, and ensuring consistency between offline training and online inference.
• Ensure models and decision systems are explainable, traceable, and well documented so that model outputs can be understood by risk operations, product stakeholders, internal governance teams, and regulators where applicable.
• Design, build, and deploy LLM-based agents for risk operations and investigation workflows, including case triage, evidence retrieval, transaction analysis, alert summarization, review recommendations, and automated action orchestration.
• Develop production-grade agent architectures using tool calling, retrieval-augmented generation, workflow orchestration, structured outputs, memory, guardrails, and human-in-the-loop controls.
• Build evaluation frameworks for LLM agents, measuring factual accuracy, task completion, decision consistency, latency, cost, reviewer acceptance, and operational impact.

Skills

Python

PyTorch

TensorFlow

XGBoost

LightGBM

scikit-learn

LLM coding tools

AI-assisted engineering

SHAP / explainability

Job description

OKX in San Jose is hiring a machine learning engineer for risk. You will design, build, and deploy models for payment fraud, account takeover, and other risk signals across a global crypto platform.

You will own end-to-end production ML systems, collaborate with risk strategy, and develop AI-assisted workflows including LLM-based agents, with a focus on safety, explainability, and scalable performance.