Senior Applied AI Solutions Engineer

What you'll do

• Create prototypes, demos, notebooks, reference architectures, and benchmarks across Nebius products and vertical use cases.
• Guide customers through POC design, technical onboarding, and validation, working directly with their ML teams.
• Investigate emerging applied AI techniques and translate customer and technical findings into product recommendations.

What they ask for

• Hands-on experience fine-tuning large models, debugging distributed training, building production RAG or agentic pipelines, and optimizing GPU inference.
• Fluency with PyTorch, HuggingFace, CUDA fundamentals, Kubernetes for ML, MLflow or equivalent, and vector databases.
• Experience working with enterprise ML teams in a solutions, customer engineering, or customer-facing ML engineering capacity.
• Reads and implements research papers; communicates technical tradeoffs to both ML engineers and executives.

Nice to have

• Experience in Physical AI, robotics, simulation, HCLS, or enterprise AI application development; familiarity with MLOps at scale.
• Prior work at a cloud provider or AI infrastructure company, or publicly shared technical work.

Pay

$200 — $350,000 USD (base compensation)

Tools

PyTorch, HuggingFace, CUDA, Kubernetes, MLflow, Kubeflow, Metaflow, Argo

AI focus

Build and demonstrate applied AI use cases; investigate training techniques, inference optimization, agentic architectures, and frameworks; support production ML workloads and shape product direction.