Senior Solutions Architect – Large Scale AI Training

NVIDIA

Senior Solutions Architect guiding EMEA AI model builders on distributed training, alignment, and NVIDIA’s full training stack. Optimizing large-scale foundation-model infrastructure and shaping product roadmaps.

Posted 9/8/2026full-timeRemote • 🇵🇱 PolandSenior💰 PLN 292,500 - PLN 507,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in distributed AI training, particularly with frameworks like Megatron-LM and NeMo, while effectively optimizing large-scale training workflows and infrastructure. Strong communication skills facilitate collaboration with research scientists and engineers to align product development with customer needs.

Highest-signal resume keywords

Distributed AI TrainingMegatron-LMNeMoReinforcement LearningHPC Environments

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Distributed Training FrameworksTraining Infrastructure OptimizationGPU UtilizationMemory ManagementFine-Tuning with Reinforcement LearningExpert Load BalancingSpeculative DecodingMulti-Node GPU ClustersLarge-Scale Training WorkflowsPost-Training Recipes

Soft Skills

Excellent Communication SkillsCollaboration

Tools & Technologies

PyTorchRL/Gym

Certifications & Qualifications

MS or PhD in Computer ScienceEngineering

Industry Keywords

AI Model BuildersAI DatacenterHPC CenterFrontier AI LabPublished WorkOpen-Source Contributions

Tech Stack

Tools & technologies

Node.jsPyTorch

About the role

Key responsibilities & impact

Build and manage strategic technical relationships with leading EMEA AI model builders developing large-scale foundation models
Define software stacks and infrastructure for large-scale training and post-training workflows, including Reinforcement Learning
Guide customers on distributed training strategies and efficient large-scale training and post-training recipes using PyTorch, Megatron-LM, or NeMo (RL/Gym)
Help customers optimize training and fine-tuning efficiency at scale, including GPU utilization, communication overlap, and memory management
Collaborate with NVIDIA product and research teams to present customer needs
Craft NVIDIA product roadmap based on customer feedback
Build or support hackathons, demos, and technical conferences to animate the developer community

Requirements

What you’ll need

MS or PhD in Computer Science, Engineering, or equivalent experience
Over 7 years of practical experience in distributed AI training
Direct involvement with HPC and/or AI environments with multi-node GPU clusters
Solid understanding of training infrastructure and its effects on efficiency and scalability
Strong proficiency with Megatron-LM, NeMo, or equivalent distributed training frameworks
Excellent communication skills with ability to engage 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
Prior experience in an AI Datacenter/HPC center, national lab, or frontier AI lab environment
Published work or open-source contributions in distributed training

Benefits

Comp & perks

Highly competitive salaries
Comprehensive benefits package
Equal opportunity employer