Pre-training Research Engineer
Sciforium· Engineering
San Francisco, CAFull-timePosted 1mo ago
About the role
Role focused on model implementation, scaling, and quality improvement for byte-native and multimodal foundation models. Build and iterate on research ideas, contribute production-grade training code and infrastructure, and help deliver base models at scale.
Responsibilities
- 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.
Qualifications
- 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.
- Strong general software engineering skills, with the ability to write robust and performant training code.
- Solid understanding of deep learning fundamentals and modern pre-training methods and literature.
- Ability to quickly implement research ideas and evaluate them using clear baselines, ablations, metrics, and analysis.
- Hands-on experience running training workloads in GPU-based environments, with familiarity with distributed training.
- MS in Computer Science, Machine Learning, Artificial Intelligence, Mathematics, or a related field.
Skills
Required:Large-scale model pre-trainingDistributed trainingResearch and experimentation
Benefits
- Medical, dental, and vision insurance
- 401k plan
- Daily lunch, snacks, and beverages
- Flexible time off
- Competitive salary and equity
Last updated Oct 23, 2026. Interested? Apply on the company site.
About Sciforium
Byte-Native AI Models & Infrastructure
Sciforium is pioneering the future of AI infrastructure and research. We're developing byte-native multimodal foundation models while delivering serverless LLM serving at a fraction of traditional costs
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