What You’ll Do
- Design and prototype data science solutions that enhance model development lifecycle (MDLC), automation, and large-scale ML enablement.
- Apply advanced machine learning and statistical techniques to solve high-impact, system-level problems with real-world constraints.
- Explore and integrate emerging trends in agentic AI and LLMs into intelligent assistants, toolkits, or reasoning systems that support internal data science use cases.
- Develop methodologies for scalable decision-making, tool orchestration, and memory-based reasoning in agentic workflows.
- Collaborate with platform engineering and product teams to translate prototypes into reusable components and frameworks.
- Stay up to date with academic and industry research and help shape the adoption of cutting-edge ML techniques into PayPal’s ecosystem.
About You
- 2+ years of industry experience as a Machine Learning Engineer, ML Scientist, or in a similar applied role.
- Solid understanding of machine learning concepts, techniques, and frameworks (e.g., PyTorch, TensorFlow, XGBoost, scikit-learn).
- Familiarity with agent frameworks or agentic system design (e.g., ReAct, AutoGPT, OpenAgents, HuggingGPT, RAG), including use of tools, memory, and planning components.
- Familiar with LLM and agentic system performance evaluation.
- Proficient in Python, with experience in building production-quality software using object-oriented programming and software engineering principles.
- Curious and passionate about staying up to date with the latest ML research and best practices.
- Strong problem-solving and communication skills, with a collaborative mindset.
- Experience in multi-team or cross-functional collaboration is a plus.
- Master’s, or PhD degree in Computer Science, Engineering, or a related technical field (or equivalent industry experience).
Our Benefits:
Any general requests for consideration of your skills, please