Staff Machine Learning Engineer

AppGate; New York (NY); On-site; USD 180,000 - 220,000; Full time

Job summary

A leading technology firm is seeking a Staff Machine Learning Engineer to lead the design of an AI-driven fraud detection platform. This role involves architecting scalable ML systems, developing end-to-end ML pipelines, and collaborating with cross-functional teams. The ideal candidate has over 5 years of experience in ML and fraud detection, expertise in Python, and knowledge of big data systems. Competitive compensation ranging from $180,000 to $220,000 plus a bonus is offered.

Qualifications

• 5+ years experience building ML or AI systems in production; at least 2 in fraud, risk, or anomaly detection.
• Proven track record designing and maintaining ML pipelines at scale.
• Strong understanding of supervised/unsupervised learning and statistical modeling.

Responsibilities

• Architect and build scalable ML systems for fraud detection.
• Develop and maintain end-to-end ML pipelines.
• Leverage modern AI techniques for fraud detection improvements.

Skills

Python

Machine Learning frameworks (PyTorch, TensorFlow, scikit-learn)

CI/CD (GitHub Actions, Jenkins)

Data collaboration and communication

Big data and distributed systems (Spark, Kafka, Flink)

Tools

AWS

GCP

Azure

Docker

Kubernetes

Job description

We are seeking an exceptional Staff Machine Learning Engineer to lead the design and development of the next generation of our AI-driven fraud detection platform.

You will architect large-scale ML systems that detect and prevent fraud in real time combining deep machine learning expertise with scalable engineering and domain knowledge in financial systems.

This is a hands‑on technical leadership role, shaping our fraud prevention roadmap and ensuring the platform evolves to meet emerging threat patterns through automation, data intelligence, and generative AI–enhanced detection models.

Responsibilities

• Architect and build scalable ML systems for fraud detection, anomaly detection, and behavioral analysis.
• Develop and maintain end-to-end ML pipelines: data ingestion, feature engineering, model training, deployment, and monitoring.
• Leverage modern AI techniques, including generative AI, to improve fraud pattern discovery and model robustness.
• Design and implement real-time decision systems, integrating with transaction or behavioral data streams.
• Collaborate closely with engineering, security, and risk teams to define data strategy and labeling frameworks.
• Lead experimentation on model explainability, drift detection, and adversarial robustness for fraud prevention use cases.
• Promote engineering excellence — automation, CI/CD, reproducibility, observability, and model governance.
• Mentor and guide ML and software engineers, fostering best practices and innovation.

Minimum Qualifications

• 5+ years of experience building ML or AI systems in production; at least 2+ in fraud, risk, or anomaly detection domains.
• Proven track record designing and maintaining ML pipelines at scale.
• Expertise in Python, ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn), and CI/CD (GitHub Actions, Jenkins, or similar).
• Strong understanding of supervised / unsupervised learning, anomaly detection, and statistical modeling.
• Experience with big data and distributed systems (e.g., Spark, Kafka, Flink, or similar).
• Familiarity with cloud platforms (AWS, GCP, or Azure) and containerized deployments (Docker, Kubernetes).
• Strong collaboration, communication, and cross-team leadership skills.

Preferred Qualifications

• Prior experience with fraud or financial crime detection, identity verification, or risk scoring systems.
• Domain expertise in banking, payments, or transaction monitoring
• Experience fine‑tuning or adapting generative AI / large language models for pattern generation or synthetic data augmentation.
• Familiarity with streaming analytics, graph ML, or time‑series anomaly detection.
• Knowledge of model governance, bias mitigation, and regulatory compliance in fraud contexts.
• Contributions to fraud detection research, open‑source, or AI publications.

What Success Looks Like

• Real‑time AI‑driven fraud prevention models with measurable reduction in false positives and detection latency.
• Scalable, automated ML pipelines enable faster experimentation and deployment.
• Cross‑functional collaboration delivering tangible business impact in fraud loss reduction.
• A culture of ML excellence, experimentation, and continuous learning across the team.

Location: New York City

Experience: 5+ years (Staff) or 8+ years (Principal) in ML or fraud detection systems

Compensation: 180-220k + bonus

Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
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