Senior Applied AI Solutions Engineer

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Nebius Group

Site WebLinkedInTous les Emplois

1001 - 5000 employés

🤖 Intelligence artificielle

🏢 Entreprise

☁️ SaaS

Artificial Intelligence • Enterprise • SaaS

Le groupe Nebius construit l’une des principales entreprises d’infrastructure AI au monde, en se concentrant sur la fourniture de la puissance de calcul, du stockage et des outils nécessaires aux développeurs dans le domaine de l’AI. Basée en Europe et cotée au Nasdaq, Nebius dispose d’une présence mondiale avec des centres de R&D en Europe, en Amérique du Nord et en Israël. L’offre principale de l’entreprise est une plateforme cloud centrée sur l’AI, conçue pour des workloads AI intensifs, complétée par diverses autres activités impliquées dans le développement de l’AI générative, l’edtech et les technologies autonomes.

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

🎯 Exigences

• 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.

🏖️ Avantages

• Competitive salary and comprehensive benefits package. • Opportunities for professional growth within Nebius. • Flexible working arrangements. • A dynamic and collaborative work environment that values initiative and innovation.