# Research Engineer - Model Evaluation & MLOps at Sciforium

- Company: Sciforium
- What the company does: Sciforium builds the next generation of AI models with unprecedented efficiency, privacy, and versatility. Backed by SignalFire.
- Company website: https://sciforium.com
- Type: Startups (AI role)
- Level: Mid level
- Location: San Francisco, CA
- Work setup: On-site
- Pay: $155K to $200K base salary per year (USD)
- Posted: 2026-08-31
- Apply by: 2026-10-15
- Apply: https://jobs.ashbyhq.com/Sciforium/8afd6b81-f865-4077-9d68-8c0ea594474a
- Page: https://www.1752.vc/careers/jobs/sciforium-research-engineer-model-evaluation-and-mlops/

## 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

Tags: Engineering