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# Machine Learning Engineer - Risk & AI

- **Company:** Okcoin Usa Inc.
- **Location:** San Jose, United States
- **Salary:** $214,666 - $321,999
- **Contract:** Permanent contract
- **Skills:** Training Data, Artificial Intelligence, Databases, Software Debugging, Fraud Prevention and Detection, Python (Programming Language), Machine Learning, Reinforcement Learning, Feature Engineering, Large Language Models, Deep Learning, AI Coding Agents, Agentic-AI, Xgboost, Machine Learning Operations, Tree-based Models, Model Explainability, Data Pipelines
- **Published:** November 12, 2026
- **Apply:** https://startup.jobs/machine-learning-engineer-risk-ai-okx-10746507

## About the Role

* A strong foundation in machine learning, with hands-on experience in deep learning and tree-based models such as XGBoost or LightGBM.
 * Strong Python skills and experience training, evaluating, and deploying models.
 * Some practical experience building AI agents or LLM applications, including tool calling, retrieval, or workflow automation. Prototypes and personal projects are welcome.
 * Regular use of AI coding tools, with the ability to understand, test, and improve generated code.
 * Familiarity with databases and data pipelines for preparing training data and supporting ML workflows.
 * Good problem-solving, communication, and collaboration skills.

Nice to Haves

 * Experience in account takeover (ATO), scam detection, or other fraud and risk use cases.
 * Experience with LLM training, fine-tuning, evaluation, and alignment.
 * Experience with reinforcement learning algorithms and their practical implementation.
 * Experience with real-time ML systems, imbalanced datasets, or model explainability.
 * Prior fraud or risk experience is helpful
 * Fluency in Mandarin

## Description

Building machine learning systems for risk at a global crypto exchange is fundamentally different from conventional ML engineering. The data spans on-chain activity, fiat deposits and withdrawals, trading behaviour, account access patterns, device intelligence, identity information, and customer interactions-signals that very few organizations can analyze together.

The problems are complex, adversarial, and constantly evolving. Models must identify emerging fraud patterns, scams, account takeovers, payment abuse, and other forms of financial risk while minimizing disruption to legitimate customers. Success is not measured only through offline model metrics. It is measured through prevented losses, improved approval rates, reduced false positives, faster investigations, and more reliable customer experience.

This role sits within a multidisciplinary risk team of machine learning engineers, data scientists, risk strategy specialists, analytics engineers, product managers, and operations teams. You will work across the full ML lifecycle - from problem formulation, feature engineering, and model development to real-time deployment, monitoring, experimentation, and continuous iteration.

You will also help shape how AI is used across the risk organization. LLM-assisted development, automated model workflows, AI-powered investigations, and intelligent review agents are part of the team's daily work. We are looking for engineers who already use these tools effectively and can help establish safe, scalable, and production-ready AI practices., We're looking for a Machine Learning Engineer to build models and AI tools that help detect fraud and protect users at a global crypto exchange. You'll apply deep learning and XGBoost, develop practical AI agents, and use AI coding tools in your daily work.

 * Build, evaluate, and deploy machine learning models for fraud and risk detection.
 * Improve data quality, features, and model performance, balancing fraud detection with customer experience.
 * Develop AI agents to support investigations, summarize cases, retrieve evidence, and automate routine workflows.
 * Use AI coding tools to accelerate development, testing, and debugging while reviewing and validating their output.
 * Work with engineering, product, and risk teams to deploy solutions and monitor their impact.