Staff Machine Learning Engineer

What You Will Be Doing

Define ML Technical Strategy: Drive the multi-quarter roadmap for ML-driven authorization optimization, routing intelligence, and retry strategies. Identify the highest-impact opportunities before they become obvious, and build the case for investment with data and business framing.

Architect Foundational ML Systems: Design and lead the implementation of shared ML infrastructure—feature stores, model serving platforms, experimentation frameworks—that accelerates every team building on payments data, not just your own.

Drive Cross-Team Technical Standards: Author and champion ML engineering standards across PPRO (model governance, monitoring, MLOps patterns), ensuring consistency, reliability, and reproducibility organization-wide.

Solve Ambiguous, High-Stakes Problems: Take on challenges where the problem itself isn't well-defined. You scope, structure, and sequence the work—then lead execution across multiple engineers and teams to deliver.

Mentor and Level Up Senior Engineers: Actively invest in the growth of Senior engineers: through design reviews, pairing on hard problems, sponsoring stretch opportunities, and raising expectations for what "production-ready ML" means at PPRO.

Lead Experimentation at Scale: Design the experimentation strategy for live payment traffic—including multi-armed bandits, causal inference approaches, and traffic-splitting frameworks—ensuring sound statistical methodology across the team.

Elevate Engineering Culture: Run design reviews, set expectations for technical documentation, and create the internal forums (guilds, working groups, RFCs) that help ML practitioners across teams learn from each other and align on standards.

What We Are Looking For

ML Architecture at Scale: Demonstrated experience designing and shipping ML systems that serve multiple products or teams—not just models, but the platforms, contracts, and abstractions that make ML reusable and reliable at scale.

Technical Leadership Without Authority: Proven ability to drive technical decisions across teams you don't manage—through clear writing, credibility, and the ability to synthesize competing perspectives into a coherent path forward.

Deep Classical & Applied ML Mastery: Expert-level command of classical ML (XGBoost, LightGBM, calibration, cost-sensitive learning) with the judgment to know when—and when not—to reach for more complex approaches. You've operated beyond standard accuracy metrics and can design evaluation frameworks appropriate to the problem.

Production ML Engineering: Extensive experience taking models from experimentation to high-throughput, low-latency production environments. You've owned reliability, SLAs, and incident response for ML systems, and you've built MLOps tooling—not just consumed it.

Software Engineering Excellence: You write and review code at a senior+ level in Python, hold the team to high standards for testability and maintainability, and can credibly engage in systems design discussions with Principal and Staff engineers across Data and Platform.

Strategic Thinking & Business Acumen: You connect technical decisions to business outcomes—approval rate improvements to revenue, latency reductions to conversion, model drift to operational risk. You communicate clearly with non-technical stakeholders and can translate ambiguous business goals into concrete ML problems.

Payments Domain Depth: Strong understanding of the card payment lifecycle, issuer behavior, authorization codes, retry logic, network rules, and 3DS. You use domain knowledge to inform feature design, model architecture, and experimentation strategy—not just as background context.

Cloud Infrastructure Mastery: Deep experience designing and owning ML infrastructure on AWS or GCP at scale, including infrastructure-as-code, cost management, and the ability to make build-vs-buy decisions on platform components.