--- title: 'Research Engineer - Model Evaluation & MLOps at Sciforium' canonical: 'https://feeny.ai/job/research-engineer-model-evaluation-mlops-sciforium-san-francisco-j6bk6vmfyth9' type: 'job' last_seen: '2026-09-13' --- # Research Engineer - Model Evaluation & MLOps at Sciforium - **Company:** Sciforium - **Location:** San Francisco, CA - **Employment:** full-time - **Work type:** onsite - **Posted:** 2026-08-31 - **Last confirmed live:** 2026-09-13 - **Apply:** https://jobs.ashbyhq.com/sciforium/8afd6b81-f865-4077-9d68-8c0ea594474a ## 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. ## About Sciforium ## Company Overview - **One-liner**: Sciforium is building a vertically integrated AI infrastructure platform that owns its hardware (AMD GPUs) to deliver cost-effective, high-performance inference and foundation model training across text, image, video, and audio modalities. - **Entity Type**: Private (Seed Stage) - **Headquarters**: San Francisco, California, United States - **Founded**: 2024 - **Founders**: Hassan Akbari ## Core Business - **Primary Industry**: AI Infrastructure / Generative AI - **Target Customers**: B2B; AI teams and enterprises that need scalable, multimodal AI inference and model serving without managing their own infrastructure. - **Mission**: To rebuild AI serving infrastructure from the ground up—owning the hardware and optimizing the entire pipeline end to end—so that any team, regardless of size or budget, can access the best AI capabilities across every modality without compromise. ## Products & Services - **AI Inference API**: A drop-in replacement for the OpenAI API format, supporting streaming, tool use, structured outputs, and async generation. Runs on Sciforium’s own AMD hardware for lower cost and stronger privacy. - **Evaluation Platform**: Built-in pipelines to monitor model performance in real time, catch regressions, and benchmark across models. - **Native Agents Infrastructure**: Serverless platform for running AI agents at scale without managing servers. - **Model Library**: Access to state-of-the-art open-source models across text, image, video, and audio (e.g., DeepSeek, Wan2, speech models). ## Market Standing - **Valuation/Market Cap**: Not disclosed - **Key Metric**: Total Funding of $3.9M (Seed round closed June 2024) - **Notable Investors/Partners**: Backed by AMD and SignalFire - **Growth Signals**: Headcount grew 160% YoY to 8 employees; 10 active job postings as of mid-2025; 99.98% uptime claimed; N+1 power and N+2 cooling redundancy with liquid-cooled infrastructure. ## Competitive Advantages - **Vertical Integration**: Owns its own AMD hardware and runs its own data centers, removing intermediaries and markups—leading to lower costs and predictable performance. - **Multimodal Native**: Built from the ground up to handle text, image, video, and audio in a single API, unlike many competitors that focus on text-only. - **Privacy & Control**: By running on dedicated infrastructure (not shared servers), customers get stronger data privacy guarantees. - **High Ambition Culture**: Team includes alumni from Google DeepMind, Microsoft, Amazon, Snowflake, Qualcomm, and Columbia University. ## Strategic Focus - **Infrastructure Ownership**: Continuing to invest in its own AMD GPU clusters and data center operations to maintain cost and performance advantages. - **Multimodal Expansion**: Scaling support for all data types (text, image, video, audio) with a single API. - **Agent Readiness**: Building native support for AI agents at scale. - **Open-Source Ecosystem**: Supporting the latest open-source models on day one. ## Why Work Here - **Culture**: Highly independent, self-motivated, and creative environment. Small enough that your work is visible from day one. Principles include relentless quality, outcome ownership, and high ambition. - **Work Policy**: Hybrid and in-office roles available. Offices in San Francisco (HQ) and Los Altos, California. - **Team**: Small, high-caliber team with deep experience from Google DeepMind, Snowflake, Amazon, Qualcomm, and other top AI/infra companies. - **Perks**: Work on hard infrastructure problems that matter, at a company backed by AMD and SignalFire. Opportunity to shape the foundation of AI infrastructure from an early stage. ## Sources 1. [sciforium.com](https://sciforium.com/) 2. [sciforium.com/company](https://sciforium.com/company) 3. [linkedin.com/company/sciforium](https://www.linkedin.com/company/sciforium) 4. [builtin.com/company/sciforium](https://builtin.com/company/sciforium) 5. 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