Staff Machine Learning Engineer
$214,666–$321,999 year
On-siteSan Jose, California, United States
Job Summary
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, managing feature pipelines, model serving, drift detection, and incident response while partnering with risk strategy to translate models into production controls. Apply AI-assisted development using LLM coding tools and develop AI-powered capabilities such as investigation agents, case summarization, and automated decision support. Ensure all models and agent systems are explainable, traceable, and secure within a regulated environment.
Required Qualifications
- 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
- 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
- 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 agentic workflows, tool calling, retrieval-augmented generation, prompt and context management, structured output generation, and multi-step task orchestration
- Experience integrating LLM agents with internal systems, APIs, databases, search tools, case-management platforms, or decision engines to automate complex operational workflows
- A strong understanding of LLM-agent evaluation and reliability, including hallucination control, grounding, observability, permissions, failure handling, human review, latency, and cost optimization
Desired Qualifications
- Experience building AI agents for fraud, risk, compliance, customer operations, cybersecurity, or other high-stakes domains
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