Staff Machine Learning Engineer

Job not on LinkedIn

🔥 14 hours ago

🇺🇸 United States – Remote

⏰ Full Time

🔴 Lead

🤖 Machine Learning Engineer

👻 Ghost score 11%

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Payabli

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2020

💼 Consulting

📣 Marketing

📦 Logistics

💰 $36M Series B - Payabli on 2025-06

Consulting • Marketing • Logistics

Payabli is a next-generation payments infrastructure company that enables software platforms to embed, manage, and monetize payments. It offers a full suite of capabilities — Pay In (payment acceptance and merchant onboarding), Pay Out (payables and payouts to vendors/partners), and Pay Ops (operations, risk, billing, and reporting) — delivered via APIs, embeddable components, and white-glove advisory services. Payabli targets SaaS companies, financial institutions, tech-enabled services, and enterprise merchants, and is a registered payment facilitator/ISO offering compliant, scalable payments solutions and developer-friendly integration tools.

📋 Description

• Set the technical direction for Payabli's model portfolio, maturing live transaction and merchant risk models and building new models across the payments lifecycle • Establish experimentation workflows, model monitoring, drift detection, performance benchmarking, and incident response foundations • Translate ambiguous payments problems into well-scoped modeling opportunities • Connect model performance to business metrics such as loss rates, approval/auth rates, dispute rates, and review efficiency • Mentor ML engineers and establish practices for the future team • Partner with product, engineering, and risk operations to own and prioritize the ML roadmap

🎯 Requirements

• 8+ years of ML engineering experience, with 4+ years building and shipping production models that drive real business decisions • Track record of owning modeling architecture and seeing big, hard-to-reverse decisions through to production • Breadth across model types and problem framing; ability to stand up a new model in an unfamiliar domain • Proven experience taking models from prototype to production and owning them post-launch, including monitoring, retraining, and incident response • Deep grasp of modeling tradeoffs including 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 • Comfortable in a fast-moving startup environment • Familiarity with AWS ML tools such as SageMaker, feature stores, training/inference pipelines, and MLOps tooling is a nice to have • Payments, fintech, or lending experience is a nice to have • Interest in growing into people leadership is a nice to have • Must be legally authorized to work in the United States • Must apply directly; no third-party recruiter or staffing agency submissions

🏖️ Benefits

• Stock options with the potential to unlock more equity as we grow • Flexible PTO and paid parental leave • Medical, dental, & vision insurance • 401K, HSA, pre-tax savings programs • Competitive salary