Senior Member of Technical Staff

Hungry, Humble, Honest, with Heart

The Opportunity

We are looking for a Senior / Staff Software Engineer to join our Nutanix Kubernetes Platform (NKP) AI Platform team. In this role, you will design, develop, and scale our next-generation platform infrastructure, which powers enterprise AI/ML workloads, GPU resource allocation, and core enterprise application environments. This platform delivers hyperscaler-like capabilities across on-prem and hybrid cloud deployments, resolving the core infrastructure and scaling bottlenecks that large enterprises face when moving complex machine learning models into production. As a technical leader, you will bridge the gap between low-level container orchestration and advanced AI/ML distributed frameworks, moving the platform toward cell-based architectures and unified global control planes.

About The Team

The NKP AI Platform team builds an enterprise-grade, production-ready platform based on Kubernetes that integrates the best of the Cloud Native Computing Foundation (CNCF) ecosystem into a comprehensive, lifecycle-managed environment. Our charter is to enable enterprises to run mission-critical and AI workloads at massive scale with minimal total cost of ownership, automated upgrades, security patching, and multi-tenant isolation. The team is globally distributed, requiring tight synchronization across geographical locations. You will report to a Senior Engineering Manager who maintains a highly collaborative leadership style, emphasizing team-wide involvement in architectural choices, objective problem-solving, and a supportive path for long-term technical advancement.

Your Role

• Design, implement, and maintain Kubernetes Custom Resource Definitions (CRDs), custom controllers, and Operators in Golang to orchestrate advanced distributed AI workloads natively.
• Build scheduling intelligence for AI-native infrastructure, optimizing GPU utilization, resource isolation, and container runtime execution for complex clusters.
• Integrate and lifecycle-manage key open-source MLOps frameworks, including Kubeflow, Ray, KServe, vLLM, and Triton Inference Server within the production platform stack.
• Provide technical leadership and mentorship across the engineering cohort by executing rigorous architectural design reviews, writing clear specs, and setting robust engineering standards.
• Collaborate cross-functionally with Core Kubernetes Platform, Product Management, and Nutanix Data Services (NDK) teams to maximize persistent storage efficiency and data gravity alignment for heavy AI datasets.

What You Will Bring

• 8+ years of professional software engineering experience, including a minimum of 3 years focused specifically on distributed systems, cloud infrastructure platforms, or AI/ML computing stacks.
• Deep proficiency in Golang for extending the Kubernetes ecosystem alongside solid proficiency in Python for ML runtime integration.
• Extensive experience analyzing Kubernetes internals, authoring robust K8s Operators or Controllers from scratch, and utilizing Cluster API (CAPI).
• Functional knowledge of distributed AI frameworks and MLOps platforms such as Kubeflow, Ray, KServe, or PyTorch Distributed Data Parallel (DDP).
• Strong systems engineering background across Linux internals, advanced networking protocols, and container runtimes including containerd and Docker.
• Demonstrated track record of designing stable, developer-friendly internal or external APIs and highly scalable microservices architectures.
• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a matching technical domain field of practical experience.

Learn More About the Technology: https://www.nutanixbible.com/

Work Arrangement

Hybrid/Remote (for roles advertised in both RTO & non-RTO locations e.g United States, Mexico, Canada): Subject to business requirements, this role may be determined to be remote or in a hybrid capacity. If the selected candidate resides within 50 miles of a Nutanix office requiring in-office presence (specifically in San Jose, Durham, Mexico City, Vancouver, Bangalore, Pune, Hoofddorp, Belgrade, Barcelona, Singapore, Sydney, or Tokyo), it will require working onsite a minimum of three days per week. Additional team-specific guidance and norms will be provided by the hiring manager.

Pay Transparency

The pay range for this position at commencement of employment is expected to be between CAD $171,200 and CAD $256,800 per annual. However, base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. The total compensation package for this position may also include other elements, including a sign-on bonus, restricted stock units, and discretionary awards in addition to a full range of medical, financial, and/or other benefits (including 401(k) eligibility and various paid time off benefits, such as vacation, sick time, and parental leave), dependent on the position offered. Details of participation in these benefit plans will be provided if an employee receives an offer of employment. If hired, employee will be in an "at-will position" and the Company reserves the right to modify base salary (as well as any other discretionary payment or compensation program) at any time, including for reasons related to individual performance, Company or individual department/team performance, and market factors. Our application deadline is 40 days from the date of posting. In good faith, the posting may be removed prior to this date if the position is filled or extended in good faith.