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JobsPayPal

Sr Machine Learning Engineer — Agentic Systems

PayPal

Sr Machine Learning Engineer — Agentic Systems

PayPal

Central Singapore, Singapore

·

On-site

·

Full-time

·

5d ago

The Company

Pay Pal has been revolutionizing commerce globally for more than 25 years. Creating innovative experiences that make moving money, selling, and shopping simple, personalized, and secure, Pay Pal 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. Pay Pal 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 Pay Pal or Venmo account balance, Pay Pal 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 Pay Pal, 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 Tensor Flow, Py Torch, 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 (Py Torch/Tensor Flow), 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:

Pay Pal

Travel Percent:

0

PayPal 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, Pay Pal'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 Pay Pal office or your home workspace, ensuring that you equally have the benefits and conveniences of both locations.

Our Benefits:

At Pay Pal, 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

Pay Pal 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, Pay Pal 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 Pay Pal:

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 Pay Pal 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 Pay Pal.

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.

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About PayPal

PayPal

PayPal

Public

PayPal Holdings, Inc. is an American multinational financial technology company operating an online payments system in the majority of countries that support online money transfers; it serves as an electronic alternative to traditional paper methods such as checks and money orders.

25,000+

Employees

San Jose

Headquarters

$70B

Valuation

Reviews

3.2

24 reviews

Work Life Balance

3.5

Compensation

2.8

Culture

2.5

Career

2.7

Management

2.2

25%

Recommend to a Friend

Pros

Good benefits and company takes care of people

Helpful colleagues and good team environment

Work life balance and flexible culture

Cons

Frequent layoffs and restructuring

Poor management and lack of direction

Low pay increases and compensation issues

Salary Ranges

3 data points

Intern

Intern · MLE Intern

1 reports

-

total / year

Base

$59

Stock

-

Bonus

-

Interview Experience

7 interviews

Difficulty

3.6

/ 5

Duration

14-28 weeks

Experience

Positive 0%

Neutral 57%

Negative 43%

Interview Process

1

Application Review

2

Online Assessment

3

Recruiter Screen

4

Technical Interview Rounds

5

System Design Interview

6

Hiring Manager Interview

7

Team Matching

Common Questions

Coding/Algorithm

System Design

Behavioral/STAR

Technical Knowledge

Past Experience