MTS 1, Machine Learning Engineer

About the position

The Machine Learning Engineer at PayPal will focus on developing innovative Machine Learning (ML) and Generative AI solutions aimed at enhancing technology and data management efficiencies. This role involves collaboration with cross-functional teams to identify process improvement opportunities and implement scalable, efficient ML systems.

Responsibilities

• Collaborate with cross-functional teams such as Data Scientists, Product Partners, and Partner Team Developers to identify opportunities for process improvements that can be solved using machine learning and generative AI. ,
• Write clean, high-performance, high-quality, maintainable code. ,
• Discover, analyze, structure, and mine data. ,
• Define and build innovative ML and Generative AI systems. ,
• Design and develop ML Engineering Solutions & generative AI Applications ensuring scalability, efficiency, and maintainability. ,
• Implement prompt engineering techniques to fine-tune and enhance LLMs for better performance and application-specific needs. ,
• Stay abreast of the latest advancements in the field of Generative AI and actively contribute to the research and development of new ML & Generative AI Solutions.

Requirements

• A Master’s or Ph.D. degree in Computer Science or a related field. ,
• Minimum 8 years of experience in Machine Learning and last 1-2 years in Generative AI solutions. ,
• Proven experience working as a Full Stack ML Engineer, with a focus on Machine Learning, ML Engineering and experience in Generative AI Applications such as RAG based AI Agents. ,
• Strong proficiency in programming languages such as Machine Learning Algorithms, Python, Hadoop & Spark. ,
• Solid knowledge of software engineering best practices, including version control systems (e.g., Git), code reviews, and testing methodologies. ,
• Experience with large language models (LLMs) & prompt engineering techniques, vector DB’s (Mandatory). ,
• Strong communication skills to effectively collaborate and present findings to both technical and non-technical stakeholders. ,
• Proven ability to adapt and learn new technologies and frameworks quickly. ,
• A proactive mindset with a passion for continuous learning and research in the field of Generative AI.

Nice-to-haves

• Experience working with various Embeddings /Embedding Models & fine-tuning LLM’s.

Benefits

• Flexible work environment ,
• Employee shares options ,
• Health insurance ,
• Life insurance ,
• Dental insurance ,
• Vision insurance ,
• Annual performance bonus ,
• Equity options