{"schemaVersion":"jobsearcher.job.v1","id":"0f880acf2f1367bb33b9896b","url":"https://jobsearcher.com/jobs/0f880acf2f1367bb33b9896b","canonicalUrl":"https://jobsearcher.com/jobs/0f880acf2f1367bb33b9896b","title":"Software Engineer: ML Optimization","description":"ML Systems Engineer — Training & Inference Optimization (MBMB)We are building large-scale embodied intelligence systems designed to operate in complex real-world environments. Our work spans robot foundation models, high-performance training infrastructure, and on-device inference systems that run directly on robotic hardware.We are seeking ML Systems Engineers to optimize both training and on-robot inference stacks. This role is focused on pushing performance boundaries across hardware, software, and model design — where improvements are still step-function rather than incremental.Internally, this team is known as MBMB (More Big More Better).What You’ll DoPush Training and Inference Performance to the LimitOptimize both large-scale training systems and on-robot inference stacksDeliver meaningful, step-function improvements in throughput, latency, and efficiencyImprove end-to-end system performance across distributed training and deployment environmentsMake GPUs Perform at Maximum EfficiencyIdentify and remove bottlenecks across the full compute stackOptimize GPU utilization across training and inference workloadsImprove performance of transformer and diffusion-based architectures under real-world constraintsEngineer Across the Full StackImplement ML, hardware-aware, and software-level optimizations that materially improve system performanceWork across:CUDA kernels and low-level GPU executionML model architecture and compute efficiencyCPU bottlenecks and data pipelinesNetwork and distributed systems performance (NVLink, interconnects, and cluster communication)Python, NumPy, and PyTorch-level inefficienciesDrive System-Level ImprovementsEvaluate and implement changes that lead to measurable gains in training and inference efficiencyCollaborate with ML researchers and systems engineers to identify high-leverage optimization opportunitiesContinuously profile, benchmark, and improve system performance across evolving workloadsWhat We’re Looking ForStrong experience with performance optimization in ML systemsUp-to-date knowledge of modern training and inference techniques for transformer and diffusion modelsAbility to reason across the full stack, including:GPU and CUDA-level optimizationModel architecture efficiencyCPU, memory, and I/O bottlenecksDistributed networking and communication overheadFramework-level performance (PyTorch, NumPy, Python)Strong systems intuition and ability to identify bottlenecks quicklyComfort operating in fast-moving environments where large performance gains are still availablePreferred ExperienceExperience optimizing large-scale training or inference systemsDeep familiarity with GPU programming and kernel optimizationExperience working with distributed ML systems at scaleExposure to model architecture-level efficiency improvementsBackground spanning both systems engineering and machine learningWhy This Role MattersDirect impact on both training speed and real-time robot performanceWork on problems where improvements are still large and measurableShape the efficiency and scalability of next-generation embodied intelligence systemsOperate across the full stack — from hardware execution to model designAbout the CompanyWe are a research-driven AI and robotics company focused on building scalable embodied intelligence systems. By combining advances in machine learning, systems engineering, and robotics, we aim to push the frontier of efficient, real-world AI.We are committed to building an inclusive and diverse workplace and encourage applicants from all backgrounds to apply.","company":"Seer","rawCompany":"seer","city":"Menlo Park","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-05-28T13:11:00.638Z","occupations":[{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"}],"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":"334111","title":"Electronic Computer Manufacturing","slug":"electronic-computer-manufacturing"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Software Engineer: ML Optimization","description":"ML Systems Engineer — Training & Inference Optimization (MBMB)We are building large-scale embodied intelligence systems designed to operate in complex real-world environments. Our work spans robot foundation models, high-performance training infrastructure, and on-device inference systems that run directly on robotic hardware.We are seeking ML Systems Engineers to optimize both training and on-robot inference stacks. This role is focused on pushing performance boundaries across hardware, software, and model design — where improvements are still step-function rather than incremental.Internally, this team is known as MBMB (More Big More Better).What You’ll DoPush Training and Inference Performance to the LimitOptimize both large-scale training systems and on-robot inference stacksDeliver meaningful, step-function improvements in throughput, latency, and efficiencyImprove end-to-end system performance across distributed training and deployment environmentsMake GPUs Perform at Maximum EfficiencyIdentify and remove bottlenecks across the full compute stackOptimize GPU utilization across training and inference workloadsImprove performance of transformer and diffusion-based architectures under real-world constraintsEngineer Across the Full StackImplement ML, hardware-aware, and software-level optimizations that materially improve system performanceWork across:CUDA kernels and low-level GPU executionML model architecture and compute efficiencyCPU bottlenecks and data pipelinesNetwork and distributed systems performance (NVLink, interconnects, and cluster communication)Python, NumPy, and PyTorch-level inefficienciesDrive System-Level ImprovementsEvaluate and implement changes that lead to measurable gains in training and inference efficiencyCollaborate with ML researchers and systems engineers to identify high-leverage optimization opportunitiesContinuously profile, benchmark, and improve system performance across evolving workloadsWhat We’re Looking ForStrong experience with performance optimization in ML systemsUp-to-date knowledge of modern training and inference techniques for transformer and diffusion modelsAbility to reason across the full stack, including:GPU and CUDA-level optimizationModel architecture efficiencyCPU, memory, and I/O bottlenecksDistributed networking and communication overheadFramework-level performance (PyTorch, NumPy, Python)Strong systems intuition and ability to identify bottlenecks quicklyComfort operating in fast-moving environments where large performance gains are still availablePreferred ExperienceExperience optimizing large-scale training or inference systemsDeep familiarity with GPU programming and kernel optimizationExperience working with distributed ML systems at scaleExposure to model architecture-level efficiency improvementsBackground spanning both systems engineering and machine learningWhy This Role MattersDirect impact on both training speed and real-time robot performanceWork on problems where improvements are still large and measurableShape the efficiency and scalability of next-generation embodied intelligence systemsOperate across the full stack — from hardware execution to model designAbout the CompanyWe are a research-driven AI and robotics company focused on building scalable embodied intelligence systems. By combining advances in machine learning, systems engineering, and robotics, we aim to push the frontier of efficient, real-world AI.We are committed to building an inclusive and diverse workplace and encourage applicants from all backgrounds to apply.","datePosted":"2026-05-28T13:11:00.638Z","dateModified":"2026-05-28T13:11:00.638Z","hiringOrganization":{"@type":"Organization","name":"Seer","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Menlo Park","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"0f880acf2f1367bb33b9896b"},"url":"https://jobsearcher.com/jobs/0f880acf2f1367bb33b9896b"}}