Senior Solutions Architect – Large Scale AI Training

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.

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.

Experience in fine-tuning with Reinforcement Learning (RLVR, RLHF) at scale.

Experience with LatentMoE, expert load balancing, and speculative decoding for MoE inference.