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Staff Machine Learning Engineer at PayPal
San Jose, California, United States -
Full Time

Start Date

Immediate

Expiry Date

03 Mar, 26

Salary

0.0

Posted On

03 Dec, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Machine Learning, Data Analysis, Model Deployment, TensorFlow, PyTorch, Scikit-learn, AWS, Azure, GCP, AI Agents, Multi-step Reasoning, Decision-making, Agent Orchestration, Performance Monitoring, Scalability, Reliability

Industry

Software Development

Description
Lead the development and optimization of advanced machine learning models. Oversee the 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, making necessary adjustments. 5+ years relevant experience and a Bachelor's degree OR Any equivalent combination of education and experience. Extensive experience with ML frameworks like TensorFlow, PyTorch, or scikit-learn. Expertise in cloud platforms (AWS, Azure, GCP) and tools for data processing and model deployment. Design, develop, and deploy autonomous AI agents capable of performing complex tasks across business workflows. Collaborate with cross-functional teams to integrate LLM-based systems into existing platforms and tools. Develop frameworks for goal-driven, multi-step reasoning and decision-making in AI agents. Implement agent orchestration, including memory, planning, and tool-use capabilities. Monitor and optimize agent performance using data-driven metrics, ensuring scalability, reliability, and compliance. Stay updated with the latest developments in LLM, RAG, and agentic system architectures, applying best practices to product innovation.
Responsibilities
Lead the development and optimization of advanced machine learning models and oversee the preprocessing and analysis of large datasets. Collaborate with cross-functional teams to integrate ML models into products and services while monitoring and evaluating the performance of deployed models.