Appgate
About the Role
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
Department:
AI / Fraud Prevention Engineering
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 This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights notice from the Department of Labor.
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
Department:
AI / Fraud Prevention Engineering
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 This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights notice from the Department of Labor.