Sr Machine Learning Engineer

PayPal PayPal · Fintech · San Jose, CA +1 · Machine Learning Engineering

Senior Machine Learning Engineer at PayPal focused on designing, developing, and implementing ML models and algorithms for complex problems. The role involves building scalable ML pipelines, deploying models into production, and collaborating with cross-functional teams. Emphasis on LLM agents, multi-agent systems, tool-use, reasoning, planning, and evaluation methodologies, with a strong requirement for productionizing research ideas.

What you'd actually do

  1. Develop and optimize machine learning models for various applications.
  2. Preprocess and analyze large datasets to extract meaningful insights.
  3. Deploy ML solutions into production environments using appropriate tools and frameworks.
  4. Collaborate with cross-functional teams to integrate ML models into products and services.
  5. Monitor and evaluate the performance of deployed models.

Skills

Required

  • 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.
  • Python is second nature.

Nice to have

  • PhD in Computer Science, AI/ML, NLP, or a related field
  • research in one or more of: LLM agents, multi-agent systems, tool-use, reasoning, planning, dialogue systems, or reinforcement learning
  • Strong engineering skills
  • Deep familiarity with agentic frameworks and architectures — LangChain, Google ADK, or custom orchestration systems.
  • Experience with LLM APIs and tool-use patterns — function calling, structured outputs, retrieval-augmented generation, chain-of-thought, and prompt engineering at scale
  • Understanding of evaluation methodology for AI systems — how to measure agent performance beyond academic benchmarks, including safety, hallucination rates, and task completion in adversarial conditions
  • Published research is a plus

What the JD emphasized

  • take a research idea from paper to production
  • built systems, not just run experiments
  • Deep familiarity with agentic frameworks and architectures
  • Experience with LLM APIs and tool-use patterns
  • Understanding of evaluation methodology for AI systems

Other signals

  • design, develop, and implement machine learning models
  • building scalable ML pipelines
  • deploying models into production environments
  • LLM agents
  • multi-agent systems
  • tool-use
  • reasoning
  • planning
  • dialogue systems
  • reinforcement learning
  • agentic frameworks and architectures
  • LLM APIs
  • prompt engineering at scale
  • evaluation methodology for AI systems