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PPRO

End-to-end payments infrastructure for businesses

Staff Machine Learning Engineer

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeMunich, GermanyNo salary listed

About the Job

Requirements

  • Demonstrated experience designing and shipping ML systems serving multiple products or teams, including reusable and reliable platforms, contracts, and abstractions.
  • Proven ability to drive technical decisions across teams without direct authority through clear writing, credibility, and synthesis of competing perspectives.
  • Expert-level command of classical machine learning, including XGBoost, LightGBM, calibration, and cost-sensitive learning; judgment about when to use complex approaches; and ability to design problem-appropriate evaluation frameworks beyond standard accuracy metrics.
  • Extensive experience taking models from experimentation to high-throughput, low-latency production environments; owning reliability, service-level agreements, and incident response for ML systems; and building MLOps tooling.
  • Senior-level Python coding and code review, high standards for testability and maintainability, and ability to engage in systems design discussions with Principal and Staff engineers across Data and Platform.
  • Ability to connect technical decisions to business outcomes, communicate with non-technical stakeholders, and translate ambiguous business goals into concrete ML problems.
  • Strong understanding of the card payment lifecycle, issuer behavior, authorization codes, retry logic, network rules, and 3DS, and ability to apply this domain knowledge to feature design, model architecture, and experimentation strategy.
  • Deep experience designing and owning ML infrastructure on AWS or GCP at scale, including infrastructure-as-code, cost management, and platform-component build-versus-buy decisions.

Responsibilities

  • Define the multi-quarter roadmap for ML-driven authorization optimization, routing intelligence, and retry strategies; identify high-impact opportunities and build the case for investment using data and business framing.
  • Design and lead implementation of shared ML infrastructure, including feature stores, model-serving platforms, and experimentation frameworks, to support teams working with payments data.
  • Author and champion organization-wide ML engineering standards for model governance, monitoring, and MLOps patterns to ensure consistency, reliability, and reproducibility.
  • Scope, structure, and sequence ambiguous, high-stakes work, then lead execution across multiple engineers and teams.
  • Mentor Senior engineers through design reviews, pairing on difficult problems, sponsoring stretch opportunities, and raising expectations for production-ready ML.
  • Design experimentation strategy for live payment traffic, including multi-armed bandits, causal inference approaches, and traffic-splitting frameworks, and ensure sound statistical methodology across the team.
  • Run design reviews, set expectations for technical documentation, and create internal forums, guilds, working groups, and RFCs to help ML practitioners learn and align on standards.

About the Company

PPRO

End-to-end payments infrastructure for businesses

PPRO is a fintech company that provides digital payments infrastructure to businesses and banks so they can scale their checkout, acquiring, and risk services through one connection. Its platform lets payment platforms, acquirers, and merchants access payment methods, fraud screening tools, and other essential products from multiple providers. Everything can be deployed and controlled with clicks, not code, while an orchestration layer directs processes and data to ensure seamless end-to-end services. Competitors rely on PPRO to consolidate services and accelerate roadmaps, reduce integration complexity, and boost conversions. The company's goal is to simplify and scale digital payments for partners by offering a single, integrated gateway to multiple providers and payment workflows.

Company size
501-1,000
Company stage
Late Stage VC
Total funding
$463.5M
Headquarters
London, United Kingdom
Founded
2006

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