{"schemaVersion":"jobsearcher.job.v1","id":"26c84e5ce661ee4847c40dd8","url":"https://jobsearcher.com/jobs/26c84e5ce661ee4847c40dd8","canonicalUrl":"https://jobsearcher.com/jobs/26c84e5ce661ee4847c40dd8","title":"Machine Learning Engineer - Pre Training","description":"About Mindbeam\nWe are building the next-generation AI infrastructure for open source and enterprise. Our work is deeply research-oriented and passionate about developing ground-breaking innovations to take state-of-the-art AI applications to the next level.\nWhat drives us is not only advancing technology, but empowering the people behind it. We are a community of researchers, engineers, and visionaries who believe that collaboration, curiosity, and openness fuel progress. If you’re motivated by impact and inspired to build tools that others can build upon, you’ll be in the right place.\nMission\nDesign and optimize large-scale pre-training systems that power Mindbeam’s generative AI models.\nRole Expectations\nBuild scalable pre-training pipelines for foundation models, optimizing throughput and efficiency.\nImplement distributed training strategies across GPUs/TPUs and high-performance clusters.\nCollaborate with researchers to translate experimental setups into production-ready workflows.\nDevelop monitoring and fault-tolerance systems to ensure reliable large-scale training.\nContinuously benchmark and tune performance across hardware and software stacks.\nBackground\nBachelor’s, Master’s, or PhD in Computer Science, Engineering, or related field—or equivalent experience.\n2+ years of experience with large-scale model training and distributed systems.\nStrong coding skills in Python and familiarity with ML frameworks (PyTorch, TensorFlow, JAX).\nExperience with GPU scheduling, memory optimization, and parallelism strategies.\nComfort with containerized and orchestrated environments (Docker/Kubernetes).\nUnderstanding of high-performance computing and networking bottlenecks.\nAbout You\nYou thrive on scale and complexity. You enjoy solving system-level bottlenecks, pushing hardware and software to their limits, and working closely with researchers to accelerate cutting-edge AI development.\nCompensation Range: $150K - $190K","company":"Mindbeam","rawCompany":"mindbeam","city":"Denver","state":"CO","isRemote":false,"isActive":false,"createdAt":"2026-08-15T12:59:25.711Z","occupations":[{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"518210","title":"Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services","slug":"computing-infrastructure-providers-data-processing-web-hosting-and-related-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Machine Learning Engineer - Pre Training","description":"About Mindbeam\nWe are building the next-generation AI infrastructure for open source and enterprise. Our work is deeply research-oriented and passionate about developing ground-breaking innovations to take state-of-the-art AI applications to the next level.\nWhat drives us is not only advancing technology, but empowering the people behind it. We are a community of researchers, engineers, and visionaries who believe that collaboration, curiosity, and openness fuel progress. If you’re motivated by impact and inspired to build tools that others can build upon, you’ll be in the right place.\nMission\nDesign and optimize large-scale pre-training systems that power Mindbeam’s generative AI models.\nRole Expectations\nBuild scalable pre-training pipelines for foundation models, optimizing throughput and efficiency.\nImplement distributed training strategies across GPUs/TPUs and high-performance clusters.\nCollaborate with researchers to translate experimental setups into production-ready workflows.\nDevelop monitoring and fault-tolerance systems to ensure reliable large-scale training.\nContinuously benchmark and tune performance across hardware and software stacks.\nBackground\nBachelor’s, Master’s, or PhD in Computer Science, Engineering, or related field—or equivalent experience.\n2+ years of experience with large-scale model training and distributed systems.\nStrong coding skills in Python and familiarity with ML frameworks (PyTorch, TensorFlow, JAX).\nExperience with GPU scheduling, memory optimization, and parallelism strategies.\nComfort with containerized and orchestrated environments (Docker/Kubernetes).\nUnderstanding of high-performance computing and networking bottlenecks.\nAbout You\nYou thrive on scale and complexity. You enjoy solving system-level bottlenecks, pushing hardware and software to their limits, and working closely with researchers to accelerate cutting-edge AI development.\nCompensation Range: $150K - $190K","datePosted":"2026-08-15T12:59:25.711Z","dateModified":"2026-08-15T12:59:25.711Z","hiringOrganization":{"@type":"Organization","name":"Mindbeam","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Denver","addressRegion":"CO","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"26c84e5ce661ee4847c40dd8"},"url":"https://jobsearcher.com/jobs/26c84e5ce661ee4847c40dd8"}}