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# Senior Applied AI Solutions Engineer

- **Company:** Nebius
- **Location:** Amsterdam, Netherlands (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Nvidia CUDA, Databases, AI Infrastructure, Pytorch, Kubernetes, HuggingFace, Machine Learning Operations, Hardware Infrastructure, Serverless Computing
- **Published:** July 15, 2026
- **Apply:** https://www.adzuna.nl/details/5670186827

## About the Role

* Experience in any of our vertical domains: Physical AI / robotics / simulation, HCLS (drug discovery, medical imaging, clinical NLP), or enterprise AI application development
 * Familiarity with MLOps at scale (Kubeflow, Metaflow, Argo, Ray)
 * Prior work at a cloud provider or AI infrastructure company
 * You've shared technical work publicly - notebooks, talks, blog posts that people actually use, 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.

## Description

* Build prototypes and demos across the product portfolio - serverless inference, databases, MLflow, MLOps, and vertical use cases in Physical AI and HCLS - that become assets for sales, product, and engineering teams
 * Support new customers hands-on through POC design, technical onboarding, and validation; act as the bridge between their ML team and the platform during the critical first months
 * Go deep on emerging applied AI - new training techniques, inference optimizations, agentic architectures, new frameworks - and turn findings into working prototypes, writeups, and product recommendations
 * Feed the product roadmap with specific, grounded feedback; be the voice of "here's what broke in three customer POCs last month and here's what needs to change"
 * Develop reusable technical assets - notebooks, reference architectures, benchmark results - that reduce onboarding friction at scale

We expect you to have:

 * You've fine-tuned large models, debugged distributed training jobs, built production RAG or agentic pipelines, and optimized inference on GPU infrastructure - not just read about it
 * You're fluent in the modern ML stack: PyTorch, HuggingFace, CUDA fundamentals, Kubernetes for ML, MLflow or equivalent, vector databases
 * You've worked with enterprise ML teams - whether as a solutions engineer, customer engineer, or an ML engineer who collaborated closely with customers
 * You read papers and implement them - not for credit, but because it's how you stay sharp
 * You communicate with calibration: you can explain activation checkpointing tradeoffs to an ML engineer in the morning and the cost implication to a CTO in the afternoon


## Related Videos

- [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding)
- [Kubernetes and Microservices with Multi-Model Databases](https://www.wearedevelopers.com/videos/382-kubernetes-and-microservices-with-multi-model-databases)
- [Understanding Kubernetes in a visual way](https://www.wearedevelopers.com/videos/100085-understanding-kubernetes-in-a-visual-way)
- [Pioneering AI Assistants in Banking](https://www.wearedevelopers.com/videos/1627-pioneering-ai-assistants-in-banking)
- [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery)
- [Serverless deployment of (large) NLP models ](https://www.wearedevelopers.com/videos/158-serverless-deployment-of-large-nlp-models)

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