Senior System Software Engineer for Cloud – GeForce NOW Platform

We are seeking a Senior System Software Engineer for Cloud to join our team. As a key member of our engineering team, you will design, build, and deploy highly scalable cloud-based solutions for GeForce NOW. Your work will craft scalable and efficient cloud services to drive Visual Computing, Deep Learning, and Artificial Intelligence.

Key Responsibilities:

• Design, build, and scale distributed cloud-based systems for a high-performance SaaS/PaaS platform.
• Collaborate cross-functionally to drive new features, optimise existing systems, and enhance overall platform reliability.
• Influence the technology stack, architecture, and development methodology.
• Drive automation, monitoring, and performance tuning.
• Mentor team members and drive best practices in Kubernetes, observability, and infrastructure automation.

Requirements:

• BS or MS in Computer Science or equivalent experience with 12+ years delivering SaaS/PaaS focusing on software development and Kubernetes infrastructure automation.
• Extensive experience with Golang and C++ (Python and Java valued) and developing/scaling backend services and microservices.
• Experience with container orchestration (Kubernetes, EKS, GKE, AKS), containers (Docker, containerd), and Infrastructure as Code (Terraform, Pulumi, Helm, Kustomize).
• Deep knowledge of cloud infrastructure, distributed system design, and Kubernetes-native patterns.
• Proven experience in developing and scaling RESTful, gRPC, MCP APIs and backend services.
• Strong self-initiative, interpersonal skills, adaptability, and experience writing testable, maintainable, performant codebases with focus on observability and collaboration skills across teams and time zones.

Preferred Qualifications:

• Experience using the latest AI tools like Codex and Claude Code.
• Experience analysing observability data to identify bottlenecks and improvement areas.
• Experience with cloud-scale analytics and data-driven optimisation of infrastructure and services.
• Proven ability to operate effectively in ambiguous or fast-changing environments, breaking down uncertainty into clear technical direction and actionable execution plans.
• Strong bias for action/iteration: able to prototype, validate, learn from feedback, and refine solutions quickly while maintaining engineering quality and operational rigor.