Sciforium builds the next generation of AI models with unprecedented efficiency, privacy, and versatility. Backed by SignalFire.
About the role
As a Pre-training Research Engineer, you’ll focus on model implementation, pertaining and scaling, and improving the quality of our byte-native and multimodal foundation models. You’ll build and iterate quickly on research ideas, contribute production-grade training code and infrastructure, and help deliver high-quality base models that can serve real-world use cases at scale.
What they're looking for
- 5+ years of experience in machine learning research or engineering, with a proven track record of developing and pre-training large language or multimodal foundation models
- Software Engineering: Strong general software engineering skills, with the ability to write robust and performant training code
- ML Foundations: Solid understanding of deep learning fundamentals and modern pre-training methods and literature
- Research and Experimentation: Ability to quickly implement research ideas and evaluate them using clear baselines, ablations, metrics, and analysis
- GPU and Distributed Training: Hands-on experience running training workloads in GPU-based environments, with familiarity with distributed training
- Education: MS in Computer Science, Machine Learning, Artificial Intelligence, Mathematics, or a related field
More about this role
Sciforium is an AI infrastructure company developing next-generation multimodal AI models and a proprietary, high-efficiency serving platform. Backed by multi-million-dollar funding and direct sponsorship from AMD with hands-on support from AMD engineers the team is scaling rapidly to build the full stack powering frontier AI models and real-time applications.
As a Pre-training Research Engineer, you’ll focus on model implementation, pertaining and scaling, and improving the quality of our byte-native and multimodal foundation models. You’ll build and iterate quickly on research ideas, contribute production-grade training code and infrastructure, and help deliver high-quality base models that can serve real-world use cases at scale.
Train large byte-native and multimodal foundation models across massive, heterogeneous corpora.
Implement and evaluate new model architectures, training objectives, and optimization methods.
Develop stable pre-training recipes and run scaling experiments for novel architectures.
Conduct ablations and analyze training dynamics, model behavior, and base-model quality.
Work with data and distributed training engineers to improve training efficiency,...
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