
Sr Machine Learning Engineer — Agentic Systems
at PayPal Holdings
Posted 8 hours ago
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We’re hiring a Senior Machine Learning Engineer to build and scale agentic AI systems for risk management in fintech. You will own end-to-end delivery of production-grade agents and ML models, improving decision quality, operational efficiency, and system reliability. This role requires deep expertise across agentic systems and classical AI/ML, with the ability to lead projects and drive technical decisions. Experience with LLMs, tool use, and production deployment is highly valued.
The Company
PayPal has been revolutionizing commerce globally for more than 25 years. Creating innovative experiences that make moving money, selling, and shopping simple, personalized, and secure, PayPal empowers consumers and businesses in approximately 200 markets to join and thrive in the global economy.
We operate a global, two-sided network at scale that connects hundreds of millions of merchants and consumers. We help merchants and consumers connect, transact, and complete payments, whether they are online or in person. PayPal is more than a connection to third-party payment networks. We provide proprietary payment solutions accepted by merchants that enable the completion of payments on our platform on behalf of our customers.
We offer our customers the flexibility to use their accounts to purchase and receive payments for goods and services, as well as the ability to transfer and withdraw funds. We enable consumers to exchange funds more safely with merchants using a variety of funding sources, which may include a bank account, a PayPal or Venmo account balance, PayPal and Venmo branded credit products, a credit card, a debit card, certain cryptocurrencies, or other stored value products such as gift cards, and eligible credit card rewards. Our PayPal, Venmo, and Xoom products also make it safer and simpler for friends and family to transfer funds to each other. We offer merchants an end-to-end payments solution that provides authorization and settlement capabilities, as well as instant access to funds and payouts. We also help merchants connect with their customers, process exchanges and returns, and manage risk. We enable consumers to engage in cross-border shopping and merchants to extend their global reach while reducing the complexity and friction involved in enabling cross-border trade.
Our beliefs are the foundation for how we conduct business every day. We live each day guided by our core values of Inclusion, Innovation, Collaboration, and Wellness. Together, our values ensure that we work together as one global team with our customers at the center of everything we do – and they push us to ensure we take care of ourselves, each other, and our communities.
Job Summary:
We’re hiring a Senior Machine Learning Engineer to build and scale agentic AI systems for risk management in fintech. You will own end-to-end delivery of production-grade agents and ML models, improving decision quality, operational efficiency, and system reliability. This role requires strong depth across agentic systems and classical AI/ML, with the ability to lead projects and drive technical decisions.Job Description:
Essential Responsibilities:
- Develop and optimize machine learning models for various applications.
- Preprocess and analyze large datasets to extract meaningful insights.
- Deploy ML solutions into production environments using appropriate tools and frameworks.
- Collaborate with cross-functional teams to integrate ML models into products and services.
- Monitor and evaluate the performance of deployed models.
Minimum Qualifications:
- 3+ years relevant experience and a Bachelor’s degree OR Any equivalent combination of education and experience.
- Experience with ML frameworks like TensorFlow, PyTorch, or scikit-learn.
- Familiarity with cloud platforms (AWS, Azure, GCP) and tools for data processing and model deployment.
- Several years of experience in designing, implementing, and deploying machine learning models.
Additional Responsibilities & Preferred Qualifications:
Responsibilities
- Own end-to-end development of agentic systems: planning, task decomposition, tool/function calling, state/memory, multi-step execution, and reliability patterns (fallbacks, retries, idempotency).
- Design, build, and productionize AI/ML models for risk management, including traditional approaches and neural networks (classification/regression, ranking, anomaly detection, time series, NLP, deep learning; transformers, embeddings, sequence models, representation learning), and integrate them into decisioning workflows.
- Build and maintain ML pipelines for training, validation, and inference, including feature generation, reproducible experiments, and automated deployment workflows.
- Implement RAG and grounding pipelines to improve accuracy and auditability (retrieval, reranking, citations/traceability, context controls).
- Establish evaluation systems: offline datasets, regression suites, online monitoring, drift detection, and error analysis for both agents and models.
- Define and implement guardrails for agent actions: tool permissions, safe completion rules, policy constraints, and human-in-the-loop patterns where needed.
- Contribute to data engineering needs: data contracts, scalable pipelines, feature generation, and data quality/lineage checks.
- Improve runtime performance and operability: latency/cost optimization, observability (metrics/logs/traces), incident response and postmortems.
Minimum qualifications
- Demonstrated track record owning and shipping multiple production AI/ML systems end-to-end, from problem framing through deployment and iteration.
- Strong expertise in agentic AI systems, including hands-on experience with LLM-based tool use and at least one of: orchestration frameworks, workflow engines, or agent evaluation frameworks.
- Strong depth in traditional AI/ML algorithms with practical experience delivering measurable business impact (feature engineering, model training/tuning, evaluation, deployment).
- Hands-on experience building and optimizing neural networks (PyTorch/TensorFlow), including embeddings/representation learning and model deployment considerations.
- Solid data engineering skills: SQL fluency, pipeline/ETL design, feature pipelines, and data quality validation.
- Strong software engineering fundamentals: system design, APIs, testing, CI/CD, and production debugging.
- Strong business acumen: ability to translate risk goals into metrics, reason about trade-offs, and communicate clearly with technical and non-technical stakeholders.
Preferred qualifications
- Direct experience in risk management domains (fraud, transaction risk, credit risk, AML, disputes/chargebacks) or other large-scale decisioning systems.
- Experience with multi-agent architectures, routing policies, planners/state machines, or policy engines.
- Experience with retrieval optimization (vector search, hybrid search, reranking) and scalable knowledge systems.
- Experience building experimentation frameworks (A/B testing, counterfactual evaluation) for risk and decisioning.
Subsidiary:
PayPalTravel Percent:
0PayPal does not charge candidates any fees for courses, applications, resume reviews, interviews, background checks, or onboarding. Any such request is a red flag and likely part of a scam. To learn more about how to identify and avoid recruitment fraud please visit https://careers.pypl.com/contact-us.
For the majority of employees, PayPal's balanced hybrid work model offers 3 days in the office for effective in-person collaboration and 2 days at your choice of either the PayPal office or your home workspace, ensuring that you equally have the benefits and conveniences of both locations.
Our Benefits:
At PayPal, we’re committed to building an equitable and inclusive global economy. And we can’t do this without our most important asset-you. That’s why we offer comprehensive, choice-based programs, to support all aspects of personal wellbeing—physical, emotional, and financial—delivering meaningful value where it matters most. We strive to create a flexible, balanced work culture with a holistic approach to benefits, including generous paid time off, healthcare coverage for you and your family, and resources to create financial security and support your mental health.
Who We Are:
Click Here to learn more about our culture and community.
Commitment to Diversity and Inclusion
PayPal provides equal employment opportunity (EEO) to all persons regardless of age, color, national origin, citizenship status, physical or mental disability, race, religion, creed, gender, sex, pregnancy, sexual orientation, gender identity and/or expression, genetic information, marital status, status with regard to public assistance, veteran status, or any other characteristic protected by federal, state, or local law. In addition, PayPal will provide reasonable accommodations for qualified individuals with disabilities. If you are unable to submit an application because of incompatible assistive technology or a disability, please contact us at paypalglobaltalentacquisition@paypal.com.
Belonging at PayPal:
Our employees are central to advancing our mission, and we strive to create an environment where everyone can do their best work with a sense of purpose and belonging. Belonging at PayPal means creating a workplace with a sense of acceptance and security where all employees feel included and valued. We are proud to have a diverse workforce reflective of the merchants, consumers, and communities that we serve, and we continue to take tangible actions to cultivate inclusivity and belonging at PayPal.
Any general requests for consideration of your skills, please Join our Talent Community.
We know the confidence gap and imposter syndrome can get in the way of meeting spectacular candidates. Please don’t hesitate to apply.

