Research Engineer
US$155,000 – US$200,000 / yr base salary
Posted 31 Aug 2026
Advertised as “Research Engineer - Model Evaluation & MLOps”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 2+ yrs
- Management level
- Individual contributor
- Employment
- On-Site
- Contract
- Full-time
| Base salary | US$155,000 – US$200,000 / yr |
|---|
Field of studyComputer Engineering, Computer Science, Machine learning (or related)
Study levelMaster's or higher
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
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Rapidly integrate new internal and open-weight language and multimodal models into our GPU evaluation and inference environments.
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Build automated benchmarks for model quality and systems performance, including latency, throughput, and memory usage.
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Create standardized, reproducible comparisons across Sciforium models, external baselines, and runtime configurations.
MLOps & Model Lifecycle
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Build and maintain experiment tracking, model registry, and versioning for models, datasets, and evaluation configurations.
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Automate the path from research checkpoints to validated deployments through CI/CD and reproducible workflows.
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Monitor model quality and systems performance, and diagnose failures or regressions across model and deployment pipelines.
Research & Systems Collaboration
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Build reusable tools that help researchers launch evaluations, compare experiments, and reproduce results.
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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.
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Experience: 2+ years of professional ML or software engineering experience, including work on production ML systems, ML platforms, or MLOps infrastructure.