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AB
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Nutanix

Software Engineer 2 - Kubernetes Backend and Platform Engineering

Vancouver, BC, Canada

$103,000 to $155,000 CAD / year · Full-time

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Job details

Pay: $103,000 to $155,000 CAD / year
Job type: Full-time
Apply by: Nov 28, 2026
Experience: Experience: Up to 3 years of software engineering experience, with at least 1 year focused on building scalable distributed systems, cloud platforms, and AI/ML infrastructure.
Posting language: English
Posted: Oct 29, 2026

Original job description

The Opportunity

At Nutanix, we are expanding the capabilities of the Nutanix Kubernetes Platform (NKP) to power the next generation of enterprise AI. This platform will serve as the foundation for AI/ML workloads, GPU infrastructure, and enterprise applications, delivering hyperscaler-like capabilities in on-prem and hybrid deployments.

As an Engineer on the NKP AI Platform team, you will be a key technical leader responsible for architecting, building, and scaling the infrastructure that allows enterprises to train, deploy, and manage mission critical modern and AI Applications effortlessly.

About The Role

This is a high-impact, hands-on engineering role. You will bridge the gap between underlying container orchestration and advanced AI/ML workloads. You will be tackling hard problems in large-scale distributed systems, evolving toward global control planes, GPU scheduling, ensuring our platform is resilient, secure, and highly performant. Drive the design of and cell-based architectures

About The Team

NKP is a complete, enterprise-grade, production-ready platform based on Kubernetes that serves modern applications. By taking the best of the Cloud Native Computing Foundation (CNCF) ecosystem, but providing it in an integrated and lifecycle-managed package, NKP delivers all the functionality necessary in production with a dramatically lowered TCO. NKP provides simplified Kubernetes operations, seamless application mobility, and integrated security and compliance. Platform engineers and IT admins can use NKP to keep all cloud native resources up-to-date with upgrades, patching, maintenance, and security. The technology and community in this industry are growing fast, new applications and solutions are coming to the market every day, new standards are becoming mature at full speed.

You will report to the Senior Engineering Manager, who adopts a collaborative leadership style that encourages team involvement in decision-making and values open discussions. The manager emphasizes the importance of strong communication and problem-solving skills, aiming to build a supportive environment for both personal and professional development.

This role does not require any travel, allowing the new hire to focus on their responsibilities within the local team while also collaborating effectively with their US counterparts through virtual meetings and communication tools.

What You Will Do (Responsibilities)

Extend Kubernetes for AI: Develop Kubernetes Custom Resource Definitions (CRDs), Operators, and Controllers in Golang to natively orchestrate AI workloads.
Optimize GPU & Hardware Utilization: Build scheduling intelligence for AI-native infrastructure, including GPU-aware scheduling, resource isolation, and workload orchestration
Integrate the MLOps Ecosystem: Seamlessly integrate and manage the lifecycle of leading open-source AI tools like Kubeflow, KServe, Ray, vLLM, and Triton Inference Server within the NKP stack.
Cross-Functional Collaboration: Collaborate effectively with team members and partner closely with Product Managers, the core NKP k8s, and Nutanix Data Services (NDK) to ensure seamless data gravity and persistent storage for AI workloads.

What You Will Bring (Requirements)

Experience: Up to 3 years of software engineering experience, with at least 1 year focused on building scalable distributed systems, cloud platforms, and AI/ML infrastructure.
Language Proficiency: Deep expertise in Golang (for K8s ecosystem) and Python (for ML ecosystem).
Kubernetes Mastery: Experience with Kubernetes internals, building K8s Operators, Controllers, and working with the Cluster API (CAPI).
AI/ML Infrastructure Knowledge: Deep knowledge of MLOps frameworks and distributed AI computing (e.g., Kubeflow, Ray, KServe, PyTorch DDP) and GPU utilization/optimization
Systems Engineering: Strong understanding of Linux internals, networking, and container runtimes (containerd, Docker).
Effective Communication and Collaboration: Strong communication skills, collaborative mindset, and desire and ability to learn fast.
Education: Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related field (or equivalent practical experience).

Nice To Haves

Contributions to CNCF projects (Kubernetes, Helm, Istio) or AI open-source projects (vLLM, Ray, Kubeflow).
Hands-on experience with deploying AI Agents and GPU scheduling
Familiarity with vector databases (Milvus, Qdrant, PGVector) and RAG (Retrieval-Augmented Generation) architectures. Experience with advanced observability stacks (Prometheus, Grafana, OpenTelemetry) tailored for AI metrics (e.g., tracking GPU utilization, token generation rates, model drift).
Track record of designing intuitive, developer-friendly APIs and scalable microservices architectures.

#NKP

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

Highlighted Benefits (Vancouver, Canada)

Retirement: RRSP with dollar-for-dollar matching up to 7% of base salary

Mental Health: Dedicated mental health coverage plus top-tier paramedical benefits

Family: Fully paid maternity and parental leave and generous bereavement leave, including time for the loss of a pet

Equity: RSUs and Employee Stock Purchase Plan at a 15% discount

Time Off: Company holidays, sick days, company wellness days, and vacation starting at 10 days

Work Arrangement

Hybrid: This role operates in a hybrid capacity, blending the benefits of remote work with the advantages of in-person collaboration. In locations where our workplace policy applies (i.e. San Jose, Durham, Mexico City, Vancouver, Bangalore, Pune, Hoofddorp, Belgrade, Barcelona, Singapore, Sydney and Tokyo), employees are expected to work onsite a minimum of 3 days per week to foster collaboration, team alignment, and access to in-office resources. Workplace type may vary based on location and team requirements. Please speak with your recruiter for details. Additional team-specific guidance and norms will be provided by your manager.

Note: Nutanix does not assist with applications for work permits or permanent residence, including provincial nomination.

Pay Transparency

The pay range for this position at commencement of employment is expected to be between CAD $103,000 and CAD $155,000 per annual. However, base pay offered may vary depending on multiple individualized factors, including market location, job-related kno wledge, 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.

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More about this job

Responsibilities

Develop Kubernetes extensions, including CRDs, Operators, and Controllers, to orchestrate AI workloads, and build GPU-aware scheduling and resource-isolation capabilities. Integrate AI/ML tools into the Nutanix Kubernetes Platform and collaborate with product, Kubernetes, and data-services teams to support persistent storage and AI workloads.

Requirements

Requires up to 3 years of software engineering experience, including at least 1 year in scalable distributed systems, cloud platforms, or AI/ML infrastructure, with strong Golang and Python skills. Candidates should have Kubernetes internals and Operator experience, knowledge of AI/ML infrastructure and GPU optimization, Linux, networking, and container runtimes, plus a relevant bachelor's or master's degree or equivalent practical experience.

Skills

Golang
Python
Kubernetes
Cluster API
Distributed Systems
AI/ML Infrastructure
GPU Scheduling And Optimization
MLOps
Linux Internals
Networking
Container Runtimes
Microservices Architecture
Observability
Communication
Cross-Functional Collaboration

Benefits

RRSP Matching Up To 7%
Mental Health Coverage
Paramedical Benefits
Paid Maternity And Parental Leave
Bereavement Leave
Restricted Stock Units
Employee Stock Purchase Plan
Company Holidays
Sick Days
Company Wellness Days
Vacation Starting At 10 Days

Visa sponsorship

Not detected in the job text

Keywords

Nutanix Kubernetes Platform
Kubernetes
Golang
Python
Custom Resource Definitions
Kubernetes Operators
Kubernetes Controllers
Cluster API
Distributed Systems
Cloud Platforms
AI/ML Infrastructure
GPU Scheduling
GPU Optimization
Resource Isolation
MLOps
Kubeflow
KServe
Ray
vLLM
Triton Inference Server
PyTorch DDP
Linux Internals
Networking
containerd
Docker
Persistent Storage
Prometheus
Grafana
OpenTelemetry
Retrieval-Augmented Generation

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