Sr Machine Learning Scientist, Agentic AI

Sr Staff Machine Learning Scientist, Agentic AI

Apply for the Sr Staff Machine Learning Scientist, Agentic AI role at Pay Pal
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Pay Pal has been revolutionizing commerce globally for more than 25 years, creating innovative experiences that make moving money, selling, and shopping simple, personalized, and secure. We operate a global, two‑sided network that connects hundreds of millions of merchants and consumers, providing proprietary payment solutions and enabling cross‑border shopping.

Pay Pal is seeking a Senior Staff Machine Learning Scientist to drive the next evolution of Venmo’s Agentic Experiences—a new paradigm redefining how users interact with Venmo through intelligent, context‑aware, and autonomous AI systems. In this role you will lead the design and development of Venmo’s Agentic interface, shaping both product vision and underlying AI architecture, from system design and orchestration to LLM fine‑tuning, context engineering, evaluation, and deployment.

• Define and drive the strategic vision for machine learning initiatives within the team.
• Lead the development and optimization of machine learning models.
• Oversee preprocessing and analysis of large datasets.
• Deploy and maintain ML solutions in production environments.
• Collaborate with cross‑functional teams to integrate ML models into products and services.
• Monitor and evaluate the performance of deployed models and make necessary adjustments.
• Mentor and guide junior engineers and data scientists.
• Ensure adherence to best practices and industry standards in ML development.

• 8+ years of relevant experience and a Bachelor’s degree, or equivalent combination of education and experience.
• Deep expertise with ML frameworks such as Tensor Flow, PyTorch, or scikit‑learn.
• Extensive experience with cloud platforms (AWS, Azure, GCP) and tools for data processing and model deployment.
• Proven track record of leading the design, implementation, and deployment of machine learning models.

• Design, develop, and evolve Venmo’s Agentic system, including reasoning, memory, and action layers to deliver dynamic, context‑aware user experiences.
• Fine‑tune large language models (LLMs) for Venmo‑specific use cases, ensuring robust alignment, safety, and personalization.
• Implement and extend agentic frameworks such as A2A, MCP, Lang Graph, or similar to enable complex, multi‑agent interactions.
• Perform advanced context and prompt engineering, optimizing model responses and orchestrating multi‑turn, multi‑source interactions.
• Collaborate closely with product, design, and platform engineering teams to shape Venmo’s next‑generation Agentic product capabilities and AI interface roadmap.
• Experiment with reinforcement learning, retrieval‑augmented generation (RAG), and online adaptation to improve agent behavior and response quality.
• Develop scalable, production‑ready LLM and agentic pipelines, including data preparation, model training, evaluation, and continuous improvement.
• Communicate insights and technical trade‑offs clearly across technical and business teams to guide decision‑making and influence Venmo’s broader AI strategy.

• Master’s degree (or higher) in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative discipline.
• 7+ years of relevant industry experience (or 6+ years with a PhD).
• Deep understanding of Transformer architectures and hands‑on experience with fine‑tuning LLMs for production use cases.
• Strong proficiency in Python and ML frameworks such as PyTorch, Tensor Flow, or JAX.
• Demonstrated experience with agentic frameworks such as A2A, MCP, Lang Graph, or Lang Chain, and an understanding of agent‑oriented design patterns.
• Experience building context‑aware conversational systems, integrating multi‑source data for reasoning and response generation.
• Familiarity with evaluation methods for LLMs and agentic systems, including prompt testing, offline metrics, and human feedback loops.
• Experience with MLOps and LLMOps workflows, including model deployment, monitoring, and iterative retraining…