
The software supply chain platform for seamless updates
JFrog, headquartered in Sunnyvale, CA, is a leading software supply chain platform that automates the release of software updates. Founded in 2008, JFrog has raised $226.5 million in funding and serves thousands of clients, including Dell, Netflix, and Facebook. With a focus on DevOps and DevSecOps,...
JFrog offers an Employee Stock Purchase Plan, comprehensive medical, dental, and vision coverage, a robust retirement plan, wellness programs, and gen...
JFrog fosters a culture centered on automation and security, emphasizing the importance of continuous software updates. The company prioritizes collab...

JFrog • Tel Aviv/ Netanya, Israel
JFrog is seeking a Technical Lead for their Core Platform team to drive the evolution of ML infrastructure. You'll work with technologies like Kubernetes and Python to manage and distribute ML models at scale. This role requires strong experience in distributed systems and ML operations.
You have a strong background in distributed systems and ML infrastructure, with hands-on experience in leading technical initiatives. Your expertise in Kubernetes and container orchestration allows you to design and evolve components for managing ML models effectively. You thrive in a collaborative, ownership-driven engineering culture and enjoy mentoring engineers to drive strong technical practices.
You possess a deep understanding of high-throughput inference and safe model rollouts, which are critical for managing ML workloads. Your experience with cloud-native infrastructure enables you to optimize performance and ensure reliability in ML operations. You are passionate about evolving core libraries and developer-facing tools, enhancing logging, observability, and visibility components.
Your background includes working with ML model serving, vector databases, and GPU orchestration, which gives you a comprehensive view of the challenges in modern ML environments. You have strong performance debugging and optimization skills, allowing you to tackle complex issues effectively. You are committed to writing maintainable, high-quality code in performance-critical areas, ensuring the success of your team’s projects.
Experience with secure software supply chain workflows is a plus, as it aligns with JFrog's mission to manage software supply chains effectively. You are eager to learn and adapt to new technologies and methodologies in the rapidly evolving field of ML and AI.
As a Technical Lead at JFrog, you will design and evolve components for managing and distributing ML/AI models and artifacts at scale. You will extend the platform to support reliable, high-performance inference and training workflows, ensuring that the systems you build can handle the demands of modern AI applications. You will lead cross-team technical initiatives, serving as a reference for distributed systems and ML infrastructure design.
You will write maintainable, high-quality code in performance-critical areas, contributing to the overall success of the Core Platform team. Your role will involve mentoring engineers, helping them grow their skills and drive strong technical practices within the team. You will collaborate closely with other teams to influence architecture and ensure that the systems you build are robust and scalable.
You will also be responsible for evolving core libraries and developer-facing tools, enhancing the overall developer experience within the organization. Your contributions will directly impact the efficiency and effectiveness of ML operations at JFrog, helping to shape the future of the platform.
At JFrog, you will be part of a dynamic team that is at the forefront of innovation in DevOps and MLOps. We offer a collaborative work environment where you can thrive and make a significant impact. You will have opportunities for professional growth and development, working alongside some of the brightest minds in the industry. We encourage you to apply even if your experience doesn't match every requirement, as we value diverse perspectives and backgrounds.
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