Senior ML Engineer

At JFrog, we’re reinventing DevOps to help the world’s greatest companies innovate -- and we want you along for the ride. This is a special place with a unique combination of brilliance, spirit, and just all-around great people. Here, if you’re willing to do more, your career can take off. And since software plays a central role in everyone’s lives, you’ll be part of an important mission. Thousands of customers, including the majority of the Fortune 100, trust JFrog to manage, accelerate, and secure their software delivery from code to production -- a concept we call “liquid software.” Wouldn't it be amazing if you could join us on our journey?

We are seeking for an experienced, hands-on Senior ML Engineer to join the Artifactory group at JFrog and lead the implementation and integration of AI/ML solutions in the product.

As a Senior ML Engineer at JFrog you will... 

• Understand our users challenges and how to integrate and implement AI/ML solutions to solve those challenges
• Work with stakeholders such as Engineers, Product Management, and Analysts to translate business problems to technical requirements and solutions
• Design, develop, and implement POCs for ML models and GenAI solutions to address business needs and objectives
• Provide technical guidance and mentorship to development teams on AI/ML technologies and practices
• Build ML capabilities from ideation to production

To be a Senior ML Engineer at JFrog you need…

• 5+ years of proven experience in ML roles that include both research and production hands on work
• 5+ years of experience writing production-grade code in Python using modern paradigms such as Event Driven Architectures, Microservices, and alike
• Proficiency in building, training, running, and evaluating deep learning models using Hugging Face, Pytorch, Tensorflow, or similar frameworks
• Proficiency in building production-grade Generative AI applications using techniques such as Multi-Agent architectures, RAG and Fine Tuning
• Experience with the entire ML model lifecycle, from ideation all the way to productization
• Experience with MLOps frameworks such as Amazon SageMaker, Vertex AI or similar for building end-to-end ML pipelines
• Deep understanding of ML theory and the techniques to apply it in the real world, such as Tuning, Feature and Error analysis, Cross-Validation, and Over/Underfitting
• Bachelor's degree or higher in Computer Science or a similar computational discipline
• Excellent problem-solving abilities and analytical skills
• Strong communication and team collaboration skills