{"schemaVersion":"jobsearcher.job.v1","id":"8406d3d982c3ef4e23b856c2","url":"https://jobsearcher.com/jobs/8406d3d982c3ef4e23b856c2","canonicalUrl":"https://jobsearcher.com/jobs/8406d3d982c3ef4e23b856c2","title":"Research Engineer - Model Evaluation & MLOps","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.\n\nAbout the role\n\nAs 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.\n\nKey Responsibilities\n\nModel Enablement & Automated Evaluation\n\nRapidly integrate new internal and open-weight language and multimodal models into our GPU evaluation and inference environments.\n\nBuild automated benchmarks for model quality and systems performance, including latency, throughput, and memory usage.\n\nCreate standardized, reproducible comparisons across Sciforium models, external baselines, and runtime configurations.\n\nMLOps & Model Lifecycle\n\nBuild and maintain experiment tracking, model registry, and versioning for models, datasets, and evaluation configurations.\n\nAutomate the path from research checkpoints to validated deployments through CI/CD and reproducible workflows.\n\nMonitor model quality and systems performance, and diagnose failures or regressions across model and deployment pipelines.\n\nResearch & Systems Collaboration\n\nBuild reusable tools that help researchers launch evaluations, compare experiments, and reproduce results.\n\nProfile end-to-end model workloads and collaborate with distributed systems, inference, and GPU kernel engineers on deeper performance issues.\n\nMust-Haves\n\nCandidates 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.\n\nExperience: 2+ years of professional ML or software engineering experience, including work on production ML systems, ML platforms, or MLOps infrastructure.\n\nSoftware Engineering: Strong Python and software engineering skills, with experience building reliable production systems.\n\nMachine Learning Expertise: Hands-on experience with PyTorch, TensorFlow, or JAX and a good understanding of modern language or multimodal model architectures.\n\nEvaluation & MLOps: Experience with model evaluation or benchmarking and core model lifecycle workflows such as experiment tracking, versioning, deployment, or monitoring.\n\nGPU 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.\n\nCommunication: Ability to document systems clearly and collaborate across research, infrastructure, and product engineering teams.\n\nEducation: MS or PhD in Computer Science, Computer Engineering, Machine Learning, or a related technical field, or equivalent practical experience.\n\nNice-to-Have\n\nFamiliarity with Hugging Face Transformers or similar model libraries.\n\nExperience enabling models on AMD GPUs and ROCm.\n\nContributions to open-source evaluation, model, or ML infrastructure projects.\n\nBenefits include\n\nMedical, dental, and vision insurance\n\n401k plan\n\nDaily lunch, snacks, and beverages\n\nFlexible time off\n\nCompetitive salary and equity\n\nEqual opportunity\n\nSciforium 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.\n\nCompensation Range: $155K - $200K","company":"Sciforium","rawCompany":"sciforium","city":"Millbrae","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-09-02T07:49:48.717Z","occupations":[{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541715","title":"Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)","slug":"research-and-development-in-the-physical-engineering-and-life-sciences-except-nanotechnology-and-biotechnology"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Research Engineer - Model Evaluation & MLOps","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.\n\nAbout the role\n\nAs 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.\n\nKey Responsibilities\n\nModel Enablement & Automated Evaluation\n\nRapidly integrate new internal and open-weight language and multimodal models into our GPU evaluation and inference environments.\n\nBuild automated benchmarks for model quality and systems performance, including latency, throughput, and memory usage.\n\nCreate standardized, reproducible comparisons across Sciforium models, external baselines, and runtime configurations.\n\nMLOps & Model Lifecycle\n\nBuild and maintain experiment tracking, model registry, and versioning for models, datasets, and evaluation configurations.\n\nAutomate the path from research checkpoints to validated deployments through CI/CD and reproducible workflows.\n\nMonitor model quality and systems performance, and diagnose failures or regressions across model and deployment pipelines.\n\nResearch & Systems Collaboration\n\nBuild reusable tools that help researchers launch evaluations, compare experiments, and reproduce results.\n\nProfile end-to-end model workloads and collaborate with distributed systems, inference, and GPU kernel engineers on deeper performance issues.\n\nMust-Haves\n\nCandidates 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.\n\nExperience: 2+ years of professional ML or software engineering experience, including work on production ML systems, ML platforms, or MLOps infrastructure.\n\nSoftware Engineering: Strong Python and software engineering skills, with experience building reliable production systems.\n\nMachine Learning Expertise: Hands-on experience with PyTorch, TensorFlow, or JAX and a good understanding of modern language or multimodal model architectures.\n\nEvaluation & MLOps: Experience with model evaluation or benchmarking and core model lifecycle workflows such as experiment tracking, versioning, deployment, or monitoring.\n\nGPU 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.\n\nCommunication: Ability to document systems clearly and collaborate across research, infrastructure, and product engineering teams.\n\nEducation: MS or PhD in Computer Science, Computer Engineering, Machine Learning, or a related technical field, or equivalent practical experience.\n\nNice-to-Have\n\nFamiliarity with Hugging Face Transformers or similar model libraries.\n\nExperience enabling models on AMD GPUs and ROCm.\n\nContributions to open-source evaluation, model, or ML infrastructure projects.\n\nBenefits include\n\nMedical, dental, and vision insurance\n\n401k plan\n\nDaily lunch, snacks, and beverages\n\nFlexible time off\n\nCompetitive salary and equity\n\nEqual opportunity\n\nSciforium 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.\n\nCompensation Range: $155K - $200K","datePosted":"2026-09-02T07:49:48.717Z","dateModified":"2026-09-02T07:49:48.717Z","hiringOrganization":{"@type":"Organization","name":"Sciforium","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Millbrae","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"8406d3d982c3ef4e23b856c2"},"url":"https://jobsearcher.com/jobs/8406d3d982c3ef4e23b856c2"}}