Sciforium builds the next generation of AI models with unprecedented efficiency, privacy, and versatility. Backed by SignalFire.
About the role
As a Research Engineer focused on Model Evaluation & MLOps, you will build the tools and infrastructure needed to evaluate, deploy, and operate multimodal foundation models reliably. You will rapidly enable Sciforium’s models and the latest open-weight models on GPUs, automate quality and performance benchmarking, and improve the MLOps workflows that connect research experiments to reliable releases.
What they're looking for
- Candidates may be stronger in some areas than others. We are looking for strong software engineering foundations, hands-on ML systems experience, and depth in at least one of model evaluation, MLOps, or model deployment
- Experience: 2+ years of professional ML or software engineering experience, including work on production ML systems, ML platforms, or MLOps infrastructure
- Software Engineering: Strong Python and software engineering skills, with experience building reliable production systems
- Machine Learning Expertise: Hands-on experience with PyTorch, TensorFlow, or JAX and a good understanding of modern language or multimodal model architectures
- Evaluation & MLOps: Experience with model evaluation or benchmarking and core model lifecycle workflows such as experiment tracking, versioning, deployment, or monitoring
- GPU Systems: Experience running, benchmarking, and debugging models with one or more GPU inference runtimes, such as vLLM, SGLang, TensorRT-LLM, or equivalent, in containerized cloud or on-premises environments
More about this role
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.
As a Research Engineer focused on Model Evaluation & MLOps, you will build the tools and infrastructure needed to evaluate, deploy, and operate multimodal foundation models reliably. You will rapidly enable Sciforium’s models and the latest open-weight models on GPUs, automate quality and performance benchmarking, and improve the MLOps workflows that connect research experiments to reliable releases.
Rapidly integrate new internal and open-weight language and multimodal models into our GPU evaluation and inference environments.
Build automated benchmarks for model quality and systems performance, including latency, throughput, and memory usage.
Create standardized, reproducible comparisons across Sciforium models, external baselines, and runtime configurations.
Build and maintain experiment tracking, model registry, and...
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