Sciforium
Pre-training Research Engineer
San Francisco, CA · Senior
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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.
Responsibilities
- Implement and evaluate new model architectures, training objectives, and optimization methods.
- Develop stable pre-training recipes and run scaling experiments for novel architectures.
- Research and Experimentation: Ability to quickly implement research ideas and evaluate them using clear baselines, ablations, metrics, and analysis.
Requirements
- 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.
Compensation
- Competitive salary and equity
Benefits
- Medical, dental, and vision insurance
- Flexible time off
- Competitive salary and equity
- 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.
- ML Foundations: Solid understanding of deep learning fundamentals and modern pre-training methods and literature.
- Education: MS in Computer Science, Machine Learning, Artificial Intelligence, Mathematics, or a related field.
- PhD in Computer Science, Machine Learning, Artificial Intelligence, Mathematics, or a related field.
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.
Visa & Work Authorization
- 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.
This listing is sourced directly from Sciforium's careers page and normalized into a canonical job model.