Senior Solutions Architect – Large Scale AI Training

About This Opportunity

We are representing a confidential technology organization seeking a Senior Solutions Architect specializing in large-scale AI training and neural network alignment. This role supports leading AI research institutions and AI-native companies across EMEA in tackling complex infrastructure and algorithmic challenges for training foundation models, including large Mixture-of-Experts (MoE) architectures.

Our talent team works with candidates interested in cutting-edge distributed AI systems and post-training workflows. You'll serve as a primary technical expert bridging research and engineering, guiding organizations on full-stack training solutions while shaping future product direction based on direct customer feedback.

Responsibilities

• Build and maintain strategic technical relationships with EMEA AI model builders developing large-scale foundation models
• Collaborate with customers to define software stack and infrastructure for large-scale training and post-training workflows, including Reinforcement Learning
• Serve as the technical authority on distributed training strategies, guiding efficient large-scale training and post-training recipes using frameworks such as PyTorch, Megatron-LM, or NeMo
• Optimize training and finetuning efficiency at scale, addressing GPU utilization, communication overlap, and memory management
• Work with product and research teams to communicate customer requirements and influence roadmap
• Engage the developer community through hackathons, demonstrations, and technical conferences

What We're Looking For

Required Qualifications:

• MS or PhD in Computer Science, Engineering, or equivalent practical experience
• 7+ years of hands-on experience in distributed AI training, with direct involvement in HPC and/or AI environments using multi-node GPU clusters
• Deep understanding of training infrastructure and its impact on efficiency and scalability
• Strong proficiency with Megatron-LM, NeMo, or equivalent distributed training frameworks
• Excellent communication skills with ability to engage both research scientists and infrastructure engineers

Preferred Qualifications:

• Experience with fine-tuning using Reinforcement Learning (RLVR, RLHF) at scale
• Expertise in LatentMoE, expert load balancing, and speculative decoding for MoE inference
• Background in AI datacenter, HPC center, national lab, or frontier AI lab environments
• Published research or open-source contributions in distributed training

How We Work with You

Candidates apply through Get A Job.ai. Our recruiting team conducts initial screening and qualification calls. We then submit qualified candidates directly to our client for their interview process. Please do not attempt to contact the client directly, as all communication is managed through our talent team to ensure a smooth process for everyone.

Pay

Compensation details will be discussed during the screening process based on location and experience.

Equal Employment Opportunity: Get A Job.ai is committed to diversity and inclusion. We do not discriminate 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.