Machine Learning Engineer (Fraud)

Location: Remote - Americas

Company

hirify.global is a commerce platform that helps entrepreneurs and enterprises start and scale businesses, serving millions of merchants in 175 countries.

What you will do

• Design, build, and ship end-to-end machine learning systems for payments fraud and risk, including data pipelines, models, and serving layers.
• Develop and productionize neural sequence models, transformers, embeddings, and tree-based models such as XGBoost and LightGBM.
• Architect safe and reliable modeling systems for a high-scale, adversarial environment.
• Own the full model lifecycle from problem scoping, feature design, training, and evaluation through deployment, monitoring, and iteration.
• Partner with engineering, product, and risk experts to translate fraud patterns into modeling problems and identify new attack vectors.
• Contribute to internal AI tooling and mentor applied scientists and machine learning engineers.

Requirements

• Significant Senior or Staff-level experience as an Applied Scientist or Machine Learning Engineer building production ML systems, not only prototypes.
• Strong coding skills in Python, Ruby, Go, or a similar production language, with experience in software engineering and system design.
• Hands-on production experience with tree-based models such as XGBoost, LightGBM, or equivalent.
• Hands-on experience with neural sequence architectures, including transformers, RNNs, attention-based models, and embeddings.
• Experience owning end-to-end ML solutions from data exploration and feature engineering through deployed and monitored services.
• Experience in fraud, risk, trust and safety, abuse detection, security, or a similar adversarial domain; alternatively, strong payments experience with motivation to specialize in risk.
• Ability to choose simple, effective modeling approaches and balance precision with practical impact.
• Strong product and systems thinking, including understanding how models affect user experience, operations, and the broader payments ecosystem.
• Ability to navigate ambiguity, form opinions from data, move projects from idea to production, and communicate with technical and non-technical stakeholders.

Nice to have

• Experience with real-time decisioning systems, streaming features, and low-latency scoring.
• Background in financial crime, KYC/KYB, chargebacks, or card network rules.
• Experience building foundation-model-style systems for non-text domains such as transactions, logs, or graphs.

Culture & Benefits

• Digital-first working environment.
• Independence, critical thinking, and personal responsibility are valued.
• Work involves rapid change, ambiguity, complexity, and a high operating pace.
• AI-enabled tools may be used to screen, select, and assess applications, with recruitment-team review of outputs.