Staff Machine Learning Engineer
Sets the technical direction for production machine learning across a payments platform, building and scaling models for risk, authorization, disputes, and forecasting. Requires 8+ years of ML engineering experience, including production model ownership and strong technical leadership.
About the job
Responsibilities
- Set the technical direction for Payabli’s machine learning model portfolio, including transaction and merchant risk models and new models across the payments lifecycle.
- Establish ML foundations, including experimentation workflows, model monitoring, drift detection, performance benchmarking, and incident response.
- Translate ambiguous payments problems into well-scoped modeling opportunities and connect model performance to business outcomes such as loss rates, authorization rates, dispute rates, and review efficiency.
- Take models from prototype through production and own them post-launch, including monitoring, retraining, and incident response.
- Make architectural decisions for modeling systems and production ML workflows.
- Mentor ML engineers and establish durable engineering and ML practices.
- Partner with product, engineering, and risk operations to prioritize and execute the ML roadmap.
Requirements
- 8+ years of machine learning engineering experience.
- 4+ years building and shipping production models that drive business decisions.
- Experience owning modeling architecture and making complex, high-impact technical decisions.
- Breadth across model types and problem framing, with the ability to build models in unfamiliar domains.
- Strong understanding of tradeoffs involving precision and recall, operational cost, explainability, latency, and regulatory or compliance requirements.
- Experience establishing ML processes and infrastructure for growing teams.
- Strong technical leadership, communication, mentoring, and collaboration skills.
- Ability to explain model behavior and business impact to non-ML stakeholders.
- Comfort working in a fast-moving startup environment with a bias toward shipping.
Nice to Have
- Payments, fintech, or lending experience, including chargebacks, merchant risk, KYC/KYB, authorization or routing.
- Experience with risk, underwriting, fraud, or credit models.
- Familiarity with AWS ML tools such as SageMaker.
- Experience with feature stores, training and inference pipelines, and MLOps tooling.
- Interest in growing into people leadership.
Compensation and Benefits
- Competitive salary.
- Stock options, with potential for additional equity as the company grows.
- Flexible paid time off and paid parental leave.
- Medical, dental, and vision insurance.
- 401(k), HSA, and pre-tax savings programs.
Skills
Machine Learning, MLOps, AWS, Amazon Sagemaker, Feature Stores, Training Pipelines, Inference Pipelines, Model Monitoring, Drift Detection, Fraud Models, Risk Modeling, Python
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