Pre-training Research Engineer
About the job
- At least 5 years of experience in machine learning research or engineering, including a track record of developing and pre-training large language or multimodal foundation models.
- Strong general software engineering skills and the ability to write robust, performant training code.
- A solid understanding of deep learning fundamentals, modern pre-training methods, and relevant literature.
- Ability to implement research ideas quickly and evaluate them using clear baselines, ablations, metrics, and analysis.
- Hands-on experience running training workloads in GPU-based environments and familiarity with distributed training.
- An MS in Computer Science, Machine Learning, Artificial Intelligence, Mathematics, or a related field.
- 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.
- Build and iterate quickly on research ideas, contribute production-grade training code and infrastructure, and help deliver high-quality base models for real-world use cases at scale.
- A PhD in Computer Science, Machine Learning, Artificial Intelligence, Mathematics, or a related field.
- Extensive experience with the JAX, Flax, and XLA stack.
- Experience with multi-node pre-training using systems such as FSDP, ZeRO, or Megatron.
- Experience developing training recipes, ablations, or scaling experiments.
- Experience owning end-to-end training and evaluation pipelines with monitoring and reproducibility.
About the company
Sciforium provides an AI infrastructure stack with a serverless inference platform, giving access to a mix of open-source and proprietary models through a single API. It runs on its own custom-optimized AMD hardware, delivering models without shared cloud servers to reduce costs, boost performance, and improve data privacy. The company differentiates itself by owning the full stack—hardware, software, and models—and by pursuing byte-native multimodal foundation models. Its goal is to simplify and speed up production-ready AI deployment while lowering costs and complexity for large-scale models.
Company Size
11-50
Company Stage
N/A
Total Funding
N/A
Headquarters
San Francisco, California
Founded
2024
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Simplify's Take
What believers are saying
- SignalFire still lists Sciforium current as of June 2026.
- LinkedIn shows aggressive hiring for GPUs, ML, and growth in August 2026.
- First tranche of GPU capacity targets October 2026, suggesting near-term productization.
What critics are saying
- October 2026 capacity depends on converting one industrial facility on schedule.
- Twelve jobs across GPU, data center, and model-serving roles signal heavy burn.
- No public customers or revenue announcements expose Sciforium to existential platform credibility risk.
What makes Sciforium unique
- AMD-backed vertical stack ties hardware, serving, and models into one platform.
- Byte-native multimodal foundation models and inference platform share the same infrastructure.
- Custom AMD GPU operations target lower TCO than shared-cloud inference competitors.
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Benefits
- Health Insurance
- Dental Insurance
- Vision Insurance
- 401(k) Retirement Plan
- Meal Benefits
- Company Equity