Senior AI / Machine Learning Engineer – Fraud Detection

Jobtailor; California (MO); On-site; USD 130,000 - 170,000; Full time

Job summary

Jobtailor is seeking an experienced machine learning engineer to build and deploy high-precision models for fraud detection and risk scoring. You will engineer risk signals from large-scale data and integrate ML features into real-time risk decisioning systems.

The role requires hands-on experience with Python, SQL, and PyTorch, plus strong software/data engineering skills. You will work with cross-functional teams to ship production-ready capabilities and explore LLMs for advanced detection use

Qualifications

• 8+ years building and operating production ML systems in fraud, abuse, risk domains.
• Practical experience with Python, SQL, and PyTorch.
• Experience guiding ML systems from feature engineering through production deployment and monitoring.
• Strong software and data engineering skills across ML, backend, and data infrastructure.
• Experience building with LLMs and AI agents for AI/generation-abuse use cases.
• Strong technical judgment, ownership, and ability to solve ambiguous, adversarial problems.
• Bachelor's degree or equivalent in Computer Science, Statistics, Mathematics, or related field.
• Device fingerprinting, identity verification, behavioral signals, network intelligence, or VPN/proxy detection preferred.
• Real-time risk evaluation and automated control systems preferred.
• Human-in-the-loop or AI-assisted evaluation systems preferred.
• Distributed systems and high-scale data pipelines preferred.
• Strong adversarial approach preferred.

Responsibilities

• Build and deploy high-precision ML models for fraud and risk scoring.
• Engineer risk signals from large-scale data sources.
• Integrate ML features into real-time risk decisioning systems.
• Apply LLMs and AI agents to expand detection and classification capabilities.
• Translate attack patterns into new models, signals, and mitigations.
• Evaluate solutions on accuracy, latency, cost, and impact.
• Own model evaluation, monitoring, and drift management.
• Partner with engineering, product, and risk teams to ship production-ready capabilities.

Skills

8+ years ML production systems

Python

SQL

PyTorch

LLMs / AI agents

Real-time risk evaluation

Adversarial problem solving

Data engineering

Education

Bachelor's degree in CS/Statistics/Math or related

Tools

Distributed Systems

Data Pipelines

ML Frameworks

AI Agents

Job description

• Build and deploy high-precision ML models for fraud and abuse detection, anomaly detection, and risk scoring
• Engineer risk signals from large-scale account, device, network, behavioral, velocity, and session data
• Integrate ML/AI features into real-time risk decisioning and automated enforcement systems
• Apply LLMs and AI agents to expand detection, investigation, and classification capabilities
• Translate emerging attack patterns and relevant research into new models, signals, and mitigations
• Evaluate solutions across accuracy, latency, cost, and customer impact
• Own model evaluation, monitoring, and drift management as attacker behavior evolves
• Partner across engineering, product, and risk teams to ship production-ready capabilities

Requirements

• 8+ years building and operating production ML systems, ideally in fraud, abuse, risk, identity, trust & safety, or other adversarial domains
• Practical experience with Python, SQL, and current ML frameworks such as PyTorch
• Experience guiding ML systems from feature engineering through production deployment and monitoring
• Strong software and data engineering skills across ML, backend, and data infrastructure
• Experience building with LLMs and/or AI agents, particularly for AI/generation-abuse use cases
• Strong technical judgment, ownership, and ability to solve ambiguous, adversarial problems
• Bachelor's degree or equivalent experience in Computer Science, Statistics, Mathematics, or a related field
• Device fingerprinting, identity verification, behavioral signals, network intelligence, or VPN/proxy detection preferred
• Real-time risk evaluation and automated control systems preferred
• Human-in-the-loop or AI-assisted evaluation systems preferred
• Distributed systems and high-scale data pipelines preferred
• Strong adversarial approach preferred

Core Competencies

Expertise in building and deploying high-precision ML models for fraud detection and risk scoring, with strong capabilities in integrating ML/AI features into real-time systems. Proficient in evaluating model performance and managing drift in adversarial environments.

Highest-signal resume keywords

• 8+ Years Building Production ML Systems
• Proficient in Python and SQL
• Experience with PyTorch and Current ML Frameworks
• Strong Software and Data Engineering Skills
• Experience with LLMs and AI Agents

Hard Skills

• Machine Learning Model Development
• Feature Engineering
• Model Evaluation and MonitoringRisk Scoring
• Anomaly Detection
• Fraud Detection
• Data Engineering
• Behavioral Signals Analysis
• Real-Time Risk Evaluation
• Automated Control Systems

Soft Skills

• Strong Technical Judgment
• Ownership
• Problem Solving

Industry Keywords

• Fraud
• Abuse
• Risk Management
• Identity Verification
• Device Fingerprinting
• Network Intelligence
• Adversarial Domains
• Human-in-the-Loop Systems

Tools & Technologies

• ML Frameworks
• Distributed Systems
• Data Pipelines
• AI Agents