Software Golang Engineer (Kubernetes)

What You'll Do

• Design and implement AI inference and model training cloud products optimized for Kubernetes - from autoscaling inference servers to distributed training jobs across GPU fleets
• Write clean, efficient, and maintainable Go code to power Kubernetes controllers, operators, and custom resources supporting AI workloads
• Build APIs, CLIs, and developer tools that simplify the deployment, lifecycle management, and monitoring of AI applications
• Develop features that optimize serverless container workflows for AI, ensuring fast cold starts, resource-efficient scaling, and workload isolation
• Contribute to system performance, reliability, and security, with a focus on AI-specific challenges such as GPU scheduling, job orchestration, and data throughput
• Stay on top of Kubernetes ecosystem advancements (e.g., K8s-native ML tooling, scheduling improvements, SIGs) and influence our product roadmap accordingly

What We're Looking For

• Strong proficiency in Go programming, with experience in Kubernetes development, including controllers and operators.
• Deep understanding of Kubernetes architecture, resource management, and container orchestration.
• Experience working with Kubernetes APIs and custom resources (CRDs).
• Solid knowledge of cloud-native technologies and frameworks, including Docker and Helm.
• Strong problem-solving skills, with a passion for tackling complex challenges in distributed systems.
• Excellent communication skills and the ability to thrive in a collaborative, team-oriented environment.

Nice to Have

• Experience with Python programming language
• Experience with developing AI/ML pipelines or integrating AI frameworks (e.g., TensorFlow, PyTorch) into Kubernetes.
• Understanding of GPU scheduling and optimization in Kubernetes environments.
• Knowledge of security best practices in Kubernetes, including role-based access control (RBAC) and container security.
• Contributions to open-source Kubernetes projects or cloud-native communities.

Benefits

At Gcore , we want you to do your best work and enjoy the journey. Our benefits are designed to support your growth, well-being, and life beyond work:

• Competitive compensation
• Flexible working hours and hybrid or remote options, depending on your role
• Work from anywhere in the world for up to 45 days per year
• Private medical insurance for you and your family*
• Extra paid vacation and sick leave days*
• Support for life’s important moments and celebrations
• Language courses to help you connect and grow
• Modern, welcoming offices with snacks, drinks, and entertainment*
• Team sports and social activities*

*Benefits may vary depending on your location.

Equal Opportunity Employer

We provide equal opportunity to all applicants without regard to race, color, religion, sex, sexual orientation, age, gender identity, gender expression, national origin, disability, or any other legally protected characteristics.

The world’s digital experiences run on something invisible: the infrastructure and software that keep them fast, reliable, and secure. At Gcore , you’ll help design and deliver that foundation for an AI-driven world.

We’re a global provider of infrastructure and software solutions for AI, cloud, network, and security, powering everything from real-time communication and streaming to enterprise AI and secure web applications. With 210+ edge locations, 50+ cloud regions, and thousands of GPUs , your work here can reach users and businesses across the globe.

You’ll collaborate with leading technology partners such as Intel, NVIDIA, Dell, and Equinix , and work on platforms that power digital products used around the world. Our vision is simple: to connect the world to AI, anywhere, anytime.

Want to work on technology that goes beyond a single product or industry? Join a global team of 550+ professionals building infrastructure and software that supports the entire digital ecosystem.

We're looking for an experienced Golang Engineer to build Kubernetes-native AI infrastructure and tooling for scalable inference and training workloads.

Originally posted on Himalayas