Sr. Machine Learning Engineer

PayPal•San Jose, CA

6d•$193,978 - $246,000•Hybrid

About The Position

PayPal, Inc. seeks Sr. Machine Learning Engineer in San Jose, CA. This role involves designing and implementing machine learning models and AI Agents for various business use cases, maintaining the existing fraud prevention system, and reviewing machine learning models and agents. The engineer will collaborate with data engineers to prepare data for modeling, develop prototypes, and conduct experiments to validate model approaches. Key responsibilities include optimizing models for performance, accuracy, and scalability in production, deploying models into live systems with software engineers, and communicating technical concepts to peers and stakeholders. The role also requires staying updated on the latest developments in machine learning and applying them as appropriate. Partial telecommuting is permitted from within a commutable distance.

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.
OR 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.
Experience with large language model (LLM) Fine-Tuning, including utilizing post-training optimization methods and designing datasets, prompts, and evaluation methodologies (6 months).
Experience with Deep Learning Frameworks: PyTorch, and distributed training libraries (DeepSpeed, Accelerate, FSDP) (6 months).
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).
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).
Experience with data engineering for LLMs: data cleaning, synthesis, augmentation, labeling automation, and dataset quality control (1 year).
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).
Production ML Systems and MLOps: model deployment, inference optimization, performance monitoring, and scaling using frameworks (vLLM, Ray Serve, Triton, Cosmos) (2 years).
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).
Experience with Cloud and Compute Infrastructure: GPU compute (A100/H100), containerization (Docker), and job orchestration tools (6 months).
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).

Responsibilities

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.