{"schemaVersion":"jobsearcher.job.v1","id":"09d7ac99dce6d05197e6337c","url":"https://jobsearcher.com/jobs/09d7ac99dce6d05197e6337c","canonicalUrl":"https://jobsearcher.com/jobs/09d7ac99dce6d05197e6337c","title":"ML Research Scientist","description":"At Ludwig Computing, we are solving the energy efficiency problem of intelligent compute. Our novel co-designed approach is optimized to deliver radical improvements in energy efficiency and performance across a wide range of AI workloads. We are building a future where high-performance computing is powered by leaner, smarter, and extremely efficient hardware and software platforms. Join us at the ground floor as we build the future of intelligent compute.\n\nAbout the Role\nWe are hiring exceptional researchers and engineers across multiple areas of AI systems and next-generation compute.\n\nWe are looking for a technically exceptional and intellectually curious ML research scientist excited about making large AI models smaller, faster, and more efficient. The applicant should be comfortable being hands-on, managing projects end-to-end, moving fluently between mathematical formulation and empirical validation, and turning research ideas into clean, well-tested code. You will work directly with the founding team to research, develop, and validate model-optimization techniques, and help align that work with the broader platform.\n\nThis is a hands-on, research-meets-engineering role at the intersection of modern ML and next-generation AI compute. Researchers excited by deep learning, model efficiency, and fast-paced early‑stage environments are encouraged to apply.\n\nResponsibilities\n\nResearch, develop, and validate techniques to compress, optimize, and accelerate large AI models while preserving accuracy.\n\nMove fluently between mathematical formulation and empirical validation — take a novel method, characterize its behavior and cost, and test it on real models.\n\nBuild high-quality prototypes in PyTorch (or a comparable framework) and the tooling needed to run, track, and reproduce experiments.\n\nEstablish rigorous baselines and design careful experiments and ablations that separate genuine gains from artifacts.\n\nRead recent literature, distill it into concrete experiments, and report findings clearly.\n\nCollaborate with the team to align algorithmic work with the broader platform.\n\nRequirements\n\nPhD or equivalent research experience in computer science, electrical engineering, machine learning, applied mathematics, statistics, information theory, physics, or a related field.\n\nStrong command of probability, statistics, optimization, and linear algebra.\n\nStrong programming skills in Python, with hands-on experience in PyTorch (or a comparable deep-learning framework).\n\nAbility to develop original ideas and turn them into well-designed computational experiments.\n\nSound experimental judgment: careful baselines, ablation design, reproducibility, and honest treatment of negative results.\n\nComfortable owning and driving projects independently, and setting technical direction under uncertainty.\n\nStrong written and verbal communication.\n\nDeep prior experience with deep learning and LLMs is valued, but a strong mathematical foundation and the drive to apply it to modern models matters more. If your background is in applied mathematics, statistics, physics, or scientific computing rather than mainstream deep learning, we still encourage you to apply.\n\nBackground in probabilistic or Bayesian machine learning.\n\nExperience with Monte Carlo methods, stochastic approximation, or uncertainty quantification.\n\nSome experience with C++ or performance-oriented programming.\n\nFamiliarity with model compression, quantization, pruning, or low-precision inference.\n\nPublications or open-source work in deep learning, applied ML, or a related area.\n\nWhat You’ll Gain\n\nHands‑on work making state-of-the-art AI models dramatically leaner and more efficient.\n\nThe chance to take principled methods from mathematics all the way to measured, real-world impact.\n\nA foundational role at an early‑stage company, working directly with the founding team.\n\nDeep collaboration with a team building next‑generation AI compute through hardware‑software co‑design and ML.\n\nPlease reach out to apply@ludwigcomputing.com for any questions.\n\n#J-18808-Ljbffr","company":"Ludwig Computing","rawCompany":"ludwig computing","city":"Millbrae","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-07-16T04:03:17.061Z","occupations":[{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-2021.00","title":"Mathematicians","slug":"mathematicians"}],"industries":[{"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"},{"code":"541690","title":"Other Scientific and Technical Consulting Services","slug":"other-scientific-and-technical-consulting-services"},{"code":"541990","title":"All Other Professional, Scientific, and Technical Services","slug":"all-other-professional-scientific-and-technical-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"ML Research Scientist","description":"At Ludwig Computing, we are solving the energy efficiency problem of intelligent compute. Our novel co-designed approach is optimized to deliver radical improvements in energy efficiency and performance across a wide range of AI workloads. We are building a future where high-performance computing is powered by leaner, smarter, and extremely efficient hardware and software platforms. Join us at the ground floor as we build the future of intelligent compute.\n\nAbout the Role\nWe are hiring exceptional researchers and engineers across multiple areas of AI systems and next-generation compute.\n\nWe are looking for a technically exceptional and intellectually curious ML research scientist excited about making large AI models smaller, faster, and more efficient. The applicant should be comfortable being hands-on, managing projects end-to-end, moving fluently between mathematical formulation and empirical validation, and turning research ideas into clean, well-tested code. You will work directly with the founding team to research, develop, and validate model-optimization techniques, and help align that work with the broader platform.\n\nThis is a hands-on, research-meets-engineering role at the intersection of modern ML and next-generation AI compute. Researchers excited by deep learning, model efficiency, and fast-paced early‑stage environments are encouraged to apply.\n\nResponsibilities\n\nResearch, develop, and validate techniques to compress, optimize, and accelerate large AI models while preserving accuracy.\n\nMove fluently between mathematical formulation and empirical validation — take a novel method, characterize its behavior and cost, and test it on real models.\n\nBuild high-quality prototypes in PyTorch (or a comparable framework) and the tooling needed to run, track, and reproduce experiments.\n\nEstablish rigorous baselines and design careful experiments and ablations that separate genuine gains from artifacts.\n\nRead recent literature, distill it into concrete experiments, and report findings clearly.\n\nCollaborate with the team to align algorithmic work with the broader platform.\n\nRequirements\n\nPhD or equivalent research experience in computer science, electrical engineering, machine learning, applied mathematics, statistics, information theory, physics, or a related field.\n\nStrong command of probability, statistics, optimization, and linear algebra.\n\nStrong programming skills in Python, with hands-on experience in PyTorch (or a comparable deep-learning framework).\n\nAbility to develop original ideas and turn them into well-designed computational experiments.\n\nSound experimental judgment: careful baselines, ablation design, reproducibility, and honest treatment of negative results.\n\nComfortable owning and driving projects independently, and setting technical direction under uncertainty.\n\nStrong written and verbal communication.\n\nDeep prior experience with deep learning and LLMs is valued, but a strong mathematical foundation and the drive to apply it to modern models matters more. If your background is in applied mathematics, statistics, physics, or scientific computing rather than mainstream deep learning, we still encourage you to apply.\n\nBackground in probabilistic or Bayesian machine learning.\n\nExperience with Monte Carlo methods, stochastic approximation, or uncertainty quantification.\n\nSome experience with C++ or performance-oriented programming.\n\nFamiliarity with model compression, quantization, pruning, or low-precision inference.\n\nPublications or open-source work in deep learning, applied ML, or a related area.\n\nWhat You’ll Gain\n\nHands‑on work making state-of-the-art AI models dramatically leaner and more efficient.\n\nThe chance to take principled methods from mathematics all the way to measured, real-world impact.\n\nA foundational role at an early‑stage company, working directly with the founding team.\n\nDeep collaboration with a team building next‑generation AI compute through hardware‑software co‑design and ML.\n\nPlease reach out to apply@ludwigcomputing.com for any questions.\n\n#J-18808-Ljbffr","datePosted":"2026-07-16T04:03:17.061Z","dateModified":"2026-07-16T04:03:17.061Z","hiringOrganization":{"@type":"Organization","name":"Ludwig Computing","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Millbrae","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"09d7ac99dce6d05197e6337c"},"url":"https://jobsearcher.com/jobs/09d7ac99dce6d05197e6337c"}}