Sr Machine Learning Engineer

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

This role focuses on designing, developing, and implementing machine learning models and algorithms, with a strong emphasis on LLM agents and multi-agent systems. The engineer will build scalable ML pipelines, deploy models into production, and integrate them into products and services, requiring experience with agentic frameworks, LLM APIs, and evaluation methodologies.

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.
  • You've built systems, not just run experiments.

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
  • LangChain, Google ADK, or custom orchestration systems.
  • function calling, structured outputs, retrieval-augmented generation, chain-of-thought, and prompt engineering at scale
  • 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

  • Strong engineering skills — you can take a research idea from paper to production.
  • Deep familiarity with agentic frameworks and architectures
  • Experience with LLM APIs and tool-use patterns
  • Understanding of evaluation methodology for AI systems

Other signals

  • Deploy ML solutions into production environments
  • integrate ML models into products and services
  • LLM agents
  • multi-agent systems
  • tool-use
  • reasoning
  • planning
  • dialogue systems
  • reinforcement learning
  • agentic frameworks and architectures
  • LLM APIs
  • retrieval-augmented generation
  • prompt engineering at scale
  • evaluation methodology for AI systems