← Back to roles

Senior Solutions Architect – Large Scale AI Training

NVIDIA Corporation

Remote - France, Remote - Switzerland, Remote - SpainRemotePosted Oct 21, 2026
Apply on NVIDIA Corporation

Overview

Open roles
2,665
At this company
See all
Similar roles
131
Same title, 14 companies
Search
Added in 30 days
647
At this company, still open

Skills named in this posting

  • PyTorch / TensorFlow
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. What you'll be doing: • 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. What we need to see: • 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. Ways to stand out from the crowd: • 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. 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/ NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law. 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.

Apply on the employer's site

Original posting: jobs.nvidia.com

Apply on NVIDIA Corporation

RISC-V ecosystem

NVIDIA Corporation in the RISC-V ecosystem

RISC-V International: Premier Board

Founded in 1993, NVIDIA is an AI computing company. The company is headquartered in Santa Clara, California.

  • NVIDIA is bringing CUDA platform support to RISC-V, i.e. allowing a RISC-V CPU to host CUDA GPU applications
  • Publicly credited the RVA23 profile as the enabler: "We wouldn't have considered porting CUDA to RISC-V without it"
  • Active in RISC-V International profile/standards work and a frequent presence in RISC-V ecosystem coverage

Same employer

View all 2,665