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# Cloud Solution Architect – Educational Content Author, Nebius Academy

Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move.

[Apply for this job](#job-application)[View company](https://jobicy.com/company/nebius.md)Share19 Sep 2026Published37Listing views3Application actions19 Oct 2026Apply before  Opportunity details

## About this role.

AI SummaryNebius is hiring a mid-level AI Solution Architect and Educational Content Author for its Academy team. The role centers on creating tutorials, sample code, videos, live coding sessions, and reference architectures for cloud, GPU, and machine-learning infrastructure. It also involves advising academic partners and designing documented Infrastructure as Code solutions with the Solutions Architect team. Candidates need hands-on cloud and distributed-computing knowledge, including Kubernetes, SLURM, Terraform, Python, PyTorch, VMs, GPU clusters, networking, and storage. This is a Europe-remote, full-time role with substantial technical communication and independent content-development responsibilities.

## Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

### Job Complexity

4/5EasyHard

### Pace & Pressure

4/5RelaxedFast-paced

### Autonomy Level

4/5GuidedFull ownership

### Communication Load

5/5IndependentCollaborative

AI insightThe position requires both practical expertise in AI cloud infrastructure and the ability to translate complex concepts into accurate, useful educational materials. It also requires partner-facing solution design across varied GPU, ML, and distributed-computing use cases.

## Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job medianHighly competitive$135,000US market range$115k–$155k0$171k

AI insightNo salary range is disclosed; "Competitive compensation" is not an actionable compensation figure. Estimated US-market annual base salary for a mid-level cloud/AI solutions architect with technical education responsibilities is approximately $115,000-$155,000 USD, with an estimated midpoint of $135,000 USD; actual compensation may vary by country, location, equity, and scope.

## Core skills

Skills and capabilities most closely associated with this opportunity.

[Cloud infrastructure](https://jobicy.com/jobs?search_keywords=Cloud%20infrastructure.md)[Distributed computing](https://jobicy.com/jobs?search_keywords=Distributed%20computing.md)[GPU clusters](https://jobicy.com/jobs?search_keywords=GPU%20clusters.md)[Kubernetes](https://jobicy.com/jobs?search_keywords=Kubernetes.md)[SLURM](https://jobicy.com/jobs?search_keywords=SLURM.md)[Terraform](https://jobicy.com/jobs?search_keywords=Terraform.md)[Infrastructure as Code](https://jobicy.com/jobs?search_keywords=Infrastructure%20as%20Code.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[PyTorch](https://jobicy.com/jobs?search_keywords=PyTorch.md)[Technical writing](https://jobicy.com/jobs?search_keywords=Technical%20writing.md)

Sample interview questionsHow would you design a tutorial for running a distributed PyTorch training workload on GPU infrastructure?I would begin by defining the learner profile and a concrete outcome, such as launching multi-node training. The tutorial would include environment setup, container and dependency configuration, data access, distributed-launch configuration, observability, cost and performance considerations, and troubleshooting steps. I would validate the complete workflow in a clean environment and publish the accompanying code in a reproducible repository.

Describe how Terraform can improve cloud solution delivery for academic partners.

Terraform enables infrastructure to be defined, reviewed, versioned, and reproduced as code. For an academic partner, I would provide modular templates for networking, storage, GPU compute, identity controls, and Kubernetes or job-scheduler deployments. This reduces manual configuration, makes environments easier to maintain, and gives students and researchers a dependable starting point.

What factors would you consider when optimizing an ML workload on a GPU cluster?

I would examine GPU utilization, batch size, data-loading throughput, storage performance, interconnect bandwidth, mixed precision, distributed-training strategy, checkpointing, and job placement. I would use profiling and monitoring data to identify bottlenecks before changing configuration. The recommended optimization would balance runtime, cost, reliability, and reproducibility.

How would you explain the choice between Kubernetes and SLURM to a technical audience?

I would explain that Kubernetes is well suited to container orchestration, services, and platform-style workloads, while SLURM is designed for scheduling batch and high-performance computing jobs across shared clusters. The best choice depends on workload behavior, user workflow, isolation needs, scheduling policies, and operational maturity. I would include a decision matrix and examples rather than presenting either platform as universally better.

How do you ensure technical educational content remains accurate as products evolve?

I would treat content as a maintained product: store examples in version control, test code regularly, assign owners, track product-release changes, and establish a review process with engineers and solution architects. I would also gather learner feedback and monitor support questions to identify unclear or outdated sections. Versioned documentation and explicit prerequisites help prevent confusion when capabilities change.

About Nebius:

Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure.

Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.

Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D.

The Role

Nebius is seeking a Middle AI Solution Architect to drive adoption of our high-performance cloud infrastructure by creating educational content. You’ll join our Nebius Academy team with a mission to help developers succeed with Nebius’ cloud offerings. In this role, you’ll focus on communicating technical capabilities and showcasing differences across Nebius products: cloud infrastructure services and Token Factory.

This unique role combines some responsibilities of an AI Solution Architect and an Educational Content Author, allowing you to develop skills and grow in different directions as a Solution Architect and/or Developer Advocate while working together with an amazing team of Nebius AI Engineers and Solution Architects.

Your responsibilities will include:

* Educational content creation
Create technical content demonstrating how to effectively use computing workloads with VMs, GPU clusters, k8s, SLURM, Soperator, etc.
* Develop sample code, tutorials, and reference architectures showcasing best practices for cloud computing and ML infrastructure
* Create video tutorials and live coding sessions demonstrating effective use of Nebius cloud infrastructure

* Helping Academy’s partners build cloud solutions
Collaborate with academic partners (universities, e.g., Stevens, MIT) to understand their requirements and develop solution architectures that align with their needs: design and document Infrastructure as Code solutions, documentation, and technical how-to guides in collaboration with the Nebius Solutions Architect Team
* Act as a trusted advisor to our academic partners, providing technical expertise on GPU cloud technologies and best practices

We expect you to have:

Technical knowledge and skills

* Strong understanding of cloud infrastructure and distributed computing principles
* Experience with virtual machines, containerization, and managing compute resources
* Experience building with IaC solutions, preferably Terraform
* Knowledge of GPU clusters and techniques for optimizing ML workloads
* Working knowledge of container orchestration systems like Kubernetes and job schedulers like SLURM
* Familiarity with infrastructure components including networking, storage optimization, and resource management
* Experience optimizing performance of diverse workloads in cloud environments
* Strong programming skills, particularly in Python, and familiarity with the PyTorch ecosystem
* Understanding of cloud infrastructure concepts and deployment patterns
* Excellent written communication skills and ability to clearly express technical ideas in text

Practical experience

* 2+ years of experience in software development, cloud engineering, DevOps, or a similar technical role
* Demonstrated experience with cloud technologies and infrastructure
* Previous work with infrastructure-as-code, containerization, and cloud environments

It will be an added bonus if you have

* Experience with MLflow, Apache Airflow, or Kubeflow
* Familiarity with cloud ML platforms like AWS, GCP, Azure ML, or NVIDIA NGC
* Experience managing hybrid cloud or on-prem GPU infrastructure
* Background working with technology partners and integrating third-party solutions
* Public presentation skills

Benefits & Perks:

* Competitive compensation
* Career growth and learning opportunities
* Flexibility and ownership
* Collaborative and innovative culture
* Opportunity to work on impactful AI projects
* International environment and talented teams

What’s it like to work at Nebius:

Fast moving – Bold thinking – Constant growth – Meaningful impact – Trust and real ownership – Opportunity to shape the future of AI

Equal Opportunity Statement:

Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law.

Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire.

If you need accommodations during the application process, please let us know.

Show more

[Apply now >](https://jobicy.com/jobs/153678-cloud-solution-architect-educational-content-author-nebius-academy.md)

>  Annual salary information is not provided for this position. Explore salary ranges for similar roles in our [Salary Directory ›](https://jobicy.com/salaries.md)

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