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Sciforium is an AI infrastructure company building next-generation multimodal AI models and a proprietary high-efficiency serving platform. Backed by multi-million-dollar funding and direct AMD sponsorship, the company is scaling rapidly to develop the full stack powering frontier AI models and real-time applications.
As a Research Engineer focused on Model Evaluation & MLOps, you will own the tools and infrastructure needed to evaluate, deploy, and operate multimodal foundation models reliably at scale.
Key responsibilities include:
MODEL ENABLEMENT & AUTOMATED EVALUATION: Rapidly integrate new internal and open-weight language and multimodal models into 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.
MLOPS & MODEL LIFECYCLE: Build and maintain experiment tracking, model registry, and versioning for models, datasets, and evaluation configurations. Automate the path from research checkpoints to validated deployments through CI/CD and reproducible workflows. Monitor model quality and systems performance, and diagnose failures or regressions across model and deployment pipelines.
RESEARCH & SYSTEMS COLLABORATION: Build reusable tools that help researchers launch evaluations, compare experiments, and reproduce results. Profile end-to-end model workloads and collaborate with distributed systems, inference, and GPU kernel engineers on deeper performance issues.
Required qualifications: 2+ years of professional ML or software engineering experience, including production ML systems, ML platforms, or MLOps infrastructure. Strong Python and software engineering skills with experience building reliable production systems. Hands-on experience with PyTorch, TensorFlow, or JAX and understanding of modern language or multimodal model architectures. Experience with model evaluation, benchmarking, and core model lifecycle workflows such as experiment tracking, versioning, deployment, or monitoring. Experience running, benchmarking, and debugging models with GPU inference runtimes such as vLLM, SGLang, or TensorRT-LLM in containerized cloud or on-premises environments. Strong communication and documentation skills with ability to collaborate across research, infrastructure, and product engineering teams. MS or PhD in Computer Science, Computer Engineering, Machine Learning, or related technical field, or equivalent practical experience.
Nice-to-have: Familiarity with Hugging Face Transformers or similar model libraries. Experience enabling models on AMD GPUs and ROCm. Contributions to open-source evaluation, model, or ML infrastructure projects.
