Job description

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. BENEFITS INCLUDE - Medical, dental, and vision insurance - 401k plan - Daily lunch, snacks, and beverages - Flexible time off - Competitive salary and equity EQUAL OPPORTUNITY Sciforium is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.

Requirements

Must-haves

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.
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.
MS or PhD in Computer Science, Machine Learning, Artificial Intelligence, Mathematics, or a related field.
Daily lunch, snacks, and beverages
Flexible time off
Competitive salary and equity

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.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningml

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401k