Job Summary:

Job Description:

PayPal, Inc. seeks Sr. Machine Learning Engineer in San Jose, CA

Job Duties: Design and implement machine learning models and AI Agents for a variety of business use cases. Maintain the existing fraud prevention system. Review machine learning models and agents. Work with data engineers to collect, clean, and prepare data for modeling. Develop prototypes and conduct experiments to validate model approaches. Optimize models for performance, accuracy, and scalability in production. Collaborate with software engineers to deploy models into live systems. Communicate technical concepts and results to peers and stakeholders. Stay informed on the latest developments in machine learning and apply them as appropriate. Partial telecommuting permitted from within a commutable distance.

Minimum Requirements: Master's degree, or foreign equivalent, in Computer Science, Control Engineering, or a closely related field, plus three years of experience in the job offered or a related occupation. Employer will accept a Bachelor's degree, or foreign equivalent, in Computer Science, Control Engineering, or a closely related field, plus five years of experience in the job offered or a related occupation.

Special Skill Requirements:

1. Experience with large language model (LLM) Fine-Tuning, including utilizing post-training optimization methods and designing datasets, prompts, and evaluation methodologies (6 months);

2. Experience with Deep Learning Frameworks: PyTorch, and distributed training libraries (DeepSpeed, Accelerate, FSDP) (6 months);

3. Experience with Prompt Engineering and Prompt Optimization: designing effective system, routing, and tool-calling prompts utilizing reflecting prompting, auto-prompting, chain-of-thought, and augmentation strategies (1 year);

4. Experience with Agentic Framework Development: implementing agent workflows using frameworks (CrewAI, AutoGen, LangGraph, ReAct, or custom agent stacks), and understanding of memory, planning, orchestration, and tool-calling patterns (6 months);

5. Experience with data engineering for LLMs: data cleaning, synthesis, augmentation, labeling automation, and dataset quality control (1 year);

6. Experience with LLM Evaluation and Experimentation: building offline and online evaluation pipelines, and using A/B testing, simulation-based evaluations, or agentic task benchmarks (1 year);

7. Production ML Systems and MLOps: model deployment, inference optimization, performance monitoring, and scaling using frameworks (vLLM, Ray Serve, Triton, Cosmos) (2 years);

8. Experience with API/ Tooling Integration: designing and integrating tool-calling interfaces, REST APIs, and function schemas for agent workflows, utilizing understanding of API governance, schema consistency, and tool maturity models (6 months);

9. Experience with Cloud and Compute Infrastructure: GPU compute (A100/H100), containerization (Docker), and job orchestration tools (6 months);

10. Experience with writing clean, scalable, production-ready code for ML pipelines and agent frameworks using the following skills: Python, code modularity, testing, debugging, and CI/CD (3 years).

Additional Responsibilities & Preferred Qualifications:

The base pay for this role will depend on where you work and the relevant experience and expertise you bring. The expected range of pay for this role by location is:

Primary Location | Pay Range:

San Jose, California | Salary: $193,978.00-246,000.00 per annum. 40 hours per week; M-F, 9:00 a.m. to 5:00 p.m.

Additional compensation for this role may include an annual performance bonus, equity, or other incentive compensation, as applicable.

Must be legally authorized to work in the U.S. without sponsorship.

Subsidiary:
PayPal

Travel Percent:
0

PayPal does not charge candidates any fees for courses, applications, resume reviews, interviews, background checks, or onboarding. When making an application directly, we will never ask you to share passwords, one-time passcodes (OTP), or verification codes. Any such request is a red flag and likely part of a scam. All communication regarding your application will come from official PayPal email domains. If you suspect fraudulent activity, please report it immediately. 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.

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.