Role Overview
We are looking for a senior Solutions Architect with strong knowledge of large-scale distributed training and Alignment of Neural Networks. The position involves supporting top EMEA AI Natives and research institutions in training/post training AI models, such as large Mixture-of-Experts (MoE) models. In this role, you will be the primary technical expert for AI organizations looking at some of the hardest infrastructure and algorithmic challenges in model training and alignment. You will bridge research and engineering, guiding customers on NVIDIA's full training stack (including Nemotron) while crafting NVIDIA product roadmap based on customers' feedback.
Responsibilities
- Build and manage strategic technical relationship with leading EMEA AI model builders developing large-scale foundation models. Collaborate closely with customers to define the software stack and infrastructure required for large-scale training and post-training workflows, including Reinforcement Learning (RL).
- Serve as the go-to expert on distributed training strategies, guiding customers through efficient large-scale training and post training recipes using Deep Learning Frameworks such as PyTorch, Megatron-LM or NeMo (RL/Gym).
- Help customers optimize training/finetuning efficiency at scale, covering GPU utilization, communication overlap, memory management at scale.
- Collaborate with NVIDIA's product and research teams to present customer needs.
- Animate the developer community by building or supporting hackathons, demos, technical conferences.
Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer www.nvidiabenefits.com/. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. For Poland: The base salary range is 292,500 PLN - 507,000 PLN.
Required Qualifications
- MS or PhD in Computer Science, Engineering, or equivalent experience.
- Over 7 years of practical experience in distributed AI training, including direct involvement with HPC and/or AI environments with multi-node GPU clusters.
- Solid understanding of training infrastructure and how it affects efficiency and scalability.
- Strong proficiency with Megatron-LM, NeMo, or equivalent distributed training frameworks.
- Excellent communication skills with an ability to engage both research scientists and infrastructure engineers.
Preferred Skills
- Experience in fine-tuning with Reinforcement Learning (RLVR, RLHF) at scale.
- Experience with LatentMoE, expert load balancing, and speculative decoding for MoE inference.
- Prior experience in an AI Datacenter/HPC center, national lab, or frontier AI lab environment.
- Published work or open-source contributions in distributed training.
