Job description

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. 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. Key Responsibilities MODEL ENABLEMENT & AUTOMATED EVALUATION - 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. 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. MUST-HAVES 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. - Communication: Ability to document systems clearly and collaborate across research, infrastructure, and product engineering teams. - Education: MS or PhD in Computer Science, Computer Engineering, Machine Learning, or a 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. BENEFITS INCLUDE - Medical, dental, and vision insurance - 401k plan - Daily lunch, snacks, and beverages - Flexible time off - Competitive salary and equity 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.

Requirements

Must-haves

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 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.
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.
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.
Communication: Ability to document systems clearly and collaborate across research, infrastructure, and product engineering teams.
Education: MS or PhD in Computer Science, Computer Engineering, Machine Learning, or a related technical field, or equivalent practical experience.
Daily lunch, snacks, and beverages
Flexible time off
Competitive salary and equity

Nice-to-haves

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.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aici/cdmlpythonmachine learningllm

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401k