Pre-training Research Engineer

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.

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.

Key Responsibilities

• Pre-training & Scaling
• 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, reliability, and scalability.
• Must-Haves
• 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.
• Nice-to-Haves
• PhD in Computer Science, Machine Learning, Artificial Intelligence, Mathematics, or a related field.
• JAX Ecosystem: Extensive experience with the JAX, Flax, and XLA stack.
• Large-Scale Distributed Training: Experience with multi-node pre-training using systems such as FSDP, ZeRO, or Megatron.
• Training Recipes and Scaling: Experience developing training recipes, ablations, or scaling experiments.
• Monitoring and Reproducibility: Experience owning end-to-end training and evaluation pipelines with monitoring and reproducibility.
• Education
• MS or PhD in Computer Science, Machine Learning, Artificial Intelligence, Mathematics, or a related field.