At JFrog, we’re reinventing DevOps and MLOps to help the world’s greatest companies innovate – and we want you along for the ride. Thousands of customers, including most of the Fortune 100, trust JFrog to manage their software supply chains - a concept we call Liquid Software.

We are looking for a hands-on Tech Lead to join the Core Platform team within JFrog ML. Our engineering teams build the foundational systems behind global artifact storage, replication, and distribution - and increasingly power the next generation of AI/ML operations and governance.
Our platform is the backbone for ML workloads: managing model binaries, versioning, and scalable runtime environments for ML and AI applications. This role combines deep distributed systems with modern ML infrastructure challenges such as high-throughput inference, safe model rollouts, and multi-cloud GPU efficiency. You will also help evolve core libraries and developer-facing tools, including logging, observability, and visibility components.

As a senior technical leader, you will influence architecture across squads, lead complex development efforts, and remain heavily hands-on.

As a Tech Lead in Core Platform in JFrog you will…

• Design and evolve components for managing and distributing ML/AI models and artifacts at scale
• Extend the platform to support reliable, high-performance inference and training workflows
• Lead cross-team technical initiatives and serve as a reference for distributed systems and ML infra design
• Write maintainable, high-quality code in performance-critical areas.
• Mentor engineers and drive strong engineering practices
• Collaborate with adjacent teams to ensure seamless end-to-end ML platform behavior
• Improve the reliability, efficiency, and observability of core services

To be a Tech Lead in Core Platform in JFrog you need...

• 7+ years building large-scale backend or distributed systems
• Strong foundation in distributed systems (consistency, replication, concurrency, fault tolerance)
• Proficiency in Java / Go or similar languages
• Hands-on experience with high-performance, scalable, and reliable systems
• Ability to lead design discussions and influence technical direction across teams
• Curiosity and willingness to work with ML systems and workload patterns
• Experience with Kubernetes, container orchestration, or cloud-native infrastructure
• Thrive in a collaborative, ownership-driven engineering culture

Bonus Points

• Experience with ML model serving, vector DBs, model versioning, or GPU orchestration
• Background in secure software supply chain workflows
• Strong performance debugging and optimization skills