Staff Applied Machine Learning Engineer (Fraud)

Location: Remote - Americas

Company

hirify.global is a commerce platform helping entrepreneurs and enterprises build and scale businesses, serving millions of merchants across 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 for transactional and behavioral data.
• Architect safe and reliable machine learning systems for high-scale, adversarial environments.
• 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 and risk surfaces.
• Mentor applied scientists and machine learning engineers on modeling, experimentation, and system design.

Requirements

• Significant Senior or Staff-level experience as an Applied Scientist or Machine Learning Engineer building production machine learning systems.
• Strong coding skills in Python, Ruby, Go, or a similar production language, including software engineering and system design.
• Deep hands-on production experience with tree-based models such as XGBoost, LightGBM, or equivalent.
• Deep experience with neural sequence architectures, including transformers, recurrent neural networks, attention-based models, and embeddings.
• Experience owning end-to-end machine learning 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; or strong payments experience with a desire to specialize in risk.
• Ability to balance simple, high-impact baseline or tree models against more complex approaches.
• Strong product and systems thinking, ability to navigate ambiguity, form opinions from data, and move projects from idea to production.
• Clear communication with technical peers 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 work environment.
• Emphasis on independence, critical thinking, resilience, and resourcefulness in an environment of rapid change and ambiguity.
• Focus on professional and personal growth and improving commerce for merchants and entrepreneurs.