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Staff Machine Learning Engineer

1 hour, 21 minutes ago
United States
Full-time
Lead
Machine Learning Engineer
Software Development
Cybersecurity Feature Engineering LightGBM Python PyTorch Scikit-learn TensorFlow XGBoost
Apply Now
OKX

OKX

OKX operates as a leading cryptocurrency exchange, providing users with a platform to buy, sell, and trade various digital assets such as Bitcoin, Ethereum, and XRP, while also offering tools for exploring Web3, decentralized finance (DeFi), and non-fu...

Diversified Financial Services
1K-5K
Founded 2017
View All Jobs 84

Description

  • Design, build, and deploy machine learning models for risk use cases including payment fraud, account takeover, scam detection, and transaction monitoring.
  • Own production ML systems end to end, including feature pipelines, training workflows, model serving, monitoring, drift detection, retraining, and incident response.
  • Partner with risk strategy and product teams to turn model outputs into production controls such as approvals, rejections, reviews, and account restrictions.
  • Work with risk operations teams to incorporate reviewer feedback, improve explainability, and refine labels and training data.
  • Apply AI-assisted development tools throughout the engineering workflow while maintaining security and review standards.
  • Develop AI-powered risk capabilities such as investigation agents, case summarization, evidence collection, alert triage, and automated decision support.
  • Take research-stage models into reliable production systems by validating feature logic, reviewing data quality, and addressing latency and scalability constraints.
  • Ensure models and decision systems are explainable, traceable, and well documented for stakeholders and regulators.
  • Design and build LLM-based agents for risk operations and investigation workflows, including case triage, evidence retrieval, and review recommendations.
  • Build evaluation frameworks for LLM agents and implement safety controls such as permissions, audit logs, privacy protections, fallbacks, and escalation paths.

Requirements

  • Significant professional experience in machine learning engineering, applied data science, or a closely related field, with a strong record of taking models from prototype to production.
  • Strong Python skills and hands-on experience with machine learning frameworks such as PyTorch, TensorFlow, XGBoost, LightGBM, or scikit-learn.
  • Strong knowledge of applied machine learning fundamentals, including supervised learning, anomaly detection, representation learning, class-imbalanced modeling, model calibration, and evaluation under changing data distributions.
  • Demonstrable fluency with AI-assisted engineering, including regular use of LLM coding tools and experience building AI-integrated workflows or applications.
  • Familiarity with model explainability techniques such as SHAP, feature attribution, reason-code generation, and model scorecards.
  • Hands-on experience designing and deploying production LLM agents, including tool calling, retrieval-augmented generation, prompt and context management, structured outputs, and multi-step orchestration.
  • Experience integrating LLM agents with internal systems, APIs, databases, search tools, case-management platforms, or decision engines.
  • Strong understanding of LLM-agent evaluation and reliability, including hallucination control, grounding, observability, permissions, failure handling, human review, latency, and cost optimization.
  • Experience building AI agents for fraud, risk, compliance, customer operations, cybersecurity, or other high-stakes domains is a meaningful advantage.
  • Strong communication and collaboration skills for working across engineering, data science, risk, product, operations, legal, and compliance stakeholders.

Benefits

  • Base salary range of $214,666 to $321,999.
  • Performance bonus may be provided.
  • Long-term incentives may be provided.
  • Full range of medical benefits.
  • Full range of financial benefits.
  • Other benefits may be included depending on the position offered.

Interested in this position?

Apply directly on the company website

Apply Now

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