The ad says“Familiarity with AWS ML tools (e.g. SageMaker), and experience with feature stores, training/inference pipelines, and MLOps tooling”
The ad says“Familiarity with AWS ML tools (e.g. SageMaker), and experience with feature stores, training/inference pipelines, and MLOps tooling”
The ad says“8+ years of ML engineering experience, with 4+ years building and shipping production models that drive real business decisions”
The ad says“Experience establishing ML processes and infrastructure that a growing team inherits”
The ad says“Experience establishing ML processes and infrastructure that a growing team inherits”
The ad says“Familiarity with AWS ML tools (e.g. SageMaker), and experience with feature stores, training/inference pipelines, and MLOps tooling”
The ad says“A track record of owning modeling architecture and seeing big, hard-to-reverse decisions through to production”
The ad says“8+ years of ML engineering experience, with 4+ years building and shipping production models that drive real business decisions”
The ad says“Familiarity with AWS ML tools (e.g. SageMaker), and experience with feature stores, training/inference pipelines, and MLOps tooling”
The ad says“Familiarity with AWS ML tools (e.g. SageMaker), and experience with feature stores, training/inference pipelines, and MLOps tooling”
Payabli is looking for a Staff Machine Learning Engineer to set the technical direction for ML at Payabli. A few models are already live and driving real decisions: reducing time-to-clear for risk reviews and scoring transactions and merchants
But that’s the starting line, not the destination. We want to build a broad portfolio of models that make payments smarter and easier: automatically choosing the best payment method, reducing disputes and chargebacks, improving authorization rates, forecasting payouts, and more
We need a technical anchor who can both raise the bar on what’s live today and stand up many new models from scratch, owning evaluation, monitoring, feature development, retraining, and a clear line from model performance to business impact
The decisions you make in your first quarter (how we build and ship models here, what “good” looks like for ML) will be foundational for years as the function scales. You’ll partner closely with product, engineering, and risk operations to find where models create the most leverage across the payments lifecycle and define what “good” means for ML at Payabli
Set the technical direction for Payabli’s model portfolio - both maturing what’s live (transaction risk, merchant risk) and building new models across the payments lifecycle (payment method optimization, dispute/chargeback reduction, authorization rate improvement, payout forecasting, and beyond)
Establish the ML foundations the team will build on: experimentation workflows, model monitoring, drift detection, performance benchmarking, and incident response
Translate ambiguous payments problems into well-scoped modeling opportunities, and model performance into business terms (loss rates, approval/auth rates, dispute rates, review efficiency)
Raise the technical bar through influence and example: mentor ML engineers and set practices the future team inherits
Partner with product, engineering, and risk operations to own and prioritize the ML roadmap
Benefits
Stock options with the potential to unlock more equity as we grow
Monthly education fund to spend on anything that enhances your skills or gets you one step closer to your passion goals
Preferred experience
8+ years of ML engineering experience, with 4+ years building and shipping production models that drive real business decisions
A high technical bar set through influence and example. You make the work and the people around you better, and you’re as comfortable in the codebase as in a design review
Proven experience taking models from prototype to production and owning them post-launch (monitoring, retraining, incident response)
A track record of owning modeling architecture and seeing big, hard-to-reverse decisions through to production
Comfortable in a fast-moving startup environment; bias toward shipping
Breadth across model types and problem framing. You can stand up a new model in an unfamiliar domain, not just optimize an existing one
Deep grasp of modeling tradeoffs: precision/recall vs. operational cost, explainability, latency, and regulatory/compliance considerations
Experience establishing ML processes and infrastructure that a growing team inherits
Ability to communicate model behavior and business impact to non-ML stakeholders
Payments, fintech, or lending experience: chargebacks, merchant risk, KYC/KYB, authorization/routing, or similar domains. Experience with risk, underwriting, fraud, or credit models specifically is a big plus
Familiarity with AWS ML tools (e.g. SageMaker), and experience with feature stores, training/inference pipelines, and MLOps tooling
An interest in growing into people leadership as the function scales. We expect technical leadership from this role from the start; whether you want to manage a team down the road is genuinely up to you, and there’s a clear path if you do
