AI Research Engineer - Pre training

In this role, you will lead and contribute to the development of large-scale pre-training systems and model architectures that enhance intelligence and efficiency. You will design experiments, build scalable training frameworks, and improve model performance through iterative research and engineering work.

• Conduct large-scale pre-training of AI models on distributed GPU clusters, ensuring scalability, stability, and performance
• Design, prototype, and optimize novel model architectures, including transformer and non-transformer approaches
• Run experiments, analyze results, and refine methodologies to improve training efficiency and model quality
• Identify and resolve bottlenecks in training systems, data pipelines, and model performance
• Improve distributed training infrastructure to support next-generation AI workloads
• Collaborate with researchers and engineers to translate experimental ideas into production-ready training systems
• Contribute to the evolution of high-performance AI training systems and frameworks

Requirements

The ideal candidate has deep expertise in AI research and large-scale model training, with strong technical foundations in machine learning, distributed systems, and deep learning frameworks. You should be comfortable working in highly complex, GPU-intensive environments and driving research from concept to implementation.

• PhD or strong academic/research background in Computer Science, Machine Learning, NLP, or related fields (preferred)
• Hands-on experience with large-scale LLM pre-training on distributed GPU infrastructure (thousands of GPUs)
• Strong understanding of transformer architectures and advanced model design techniques
• Experience with distributed training frameworks and large-scale AI systems
• Proficiency in PyTorch and Hugging Face ecosystem for model development and training
• Strong skills in debugging, optimizing, and improving model and system performance
• Ability to design experiments, interpret results, and iterate on research hypotheses
• Strong collaboration and communication skills in research-driven environments

Benefits

• Competitive compensation package aligned with AI research market standards
• Remote-friendly and globally distributed work environment
• Opportunity to work on frontier AI research at massive scale
• Access to high-performance computing infrastructure and large GPU clusters
• Collaborative environment with top-tier AI researchers and engineers
• High autonomy in research direction and experimentation
• Exposure to state-of-the-art AI systems and multimodal model development
• Professional growth in a fast-evolving, innovation-driven AI ecosystem