{"schemaVersion":"jobsearcher.job.v1","id":"0f060019cfeca596ae761bd6","url":"https://jobsearcher.com/jobs/0f060019cfeca596ae761bd6","canonicalUrl":"https://jobsearcher.com/jobs/0f060019cfeca596ae761bd6","title":"Machine Learning Infrastructure Engineer","description":"About Zensors\r\nZensors is the spatial intelligence platform for the physical world. Our AI platform provides real-time insights—from airport queue times to office utilization—helping organizations make smarter operational decisions.\r\nZensors is processing massive streams of video data 24/7 with human-level accuracy. To do this at scale, we rely on cutting-edge optimization to ensure our vision transformer and detection models run efficiently on both cloud and edge compute resources.\r\nResponsibility\r\nThe AI Infrastructure team at Zensors builds the engine that powers our visual sensing platform. We provide the tools to automate the lifecycle of our AI workflow, including model development, evaluation, optimization, deployment, and monitoring across thousands of video streams.\r\nAs a Machine Learning Engineer in ML Runtime & Optimization , you will develop technologies to accelerate the training and inference of computer vision models that power smart spaces and cities.\r\nYour responsibilities will include\r\nOptimizing Core ML Pipelines: Identifying key bottlenecks in our current video analytics pipeline and performing in-depth analysis to ensure the best possible performance on current server and edge compute architectures.\r\nCross-Stack Collaboration: Collaborating closely with AI research and platform engineering teams to optimize core parallel algorithms and influence the design of our next-generation inference infrastructure.\r\nModel Acceleration: Applying advanced model optimization techniques—such as quantization (Int8/FP16), pruning, and layer fusion—to our Vision Transformers (ViTs) and CNNs to maximize throughput and minimize latency.\r\nBuilding Efficient Operators: Working across the entire ML framework/compiler stack (e.g., PyTorch, CUDA, TensorRT, and NVIDIA DeepStream) to write custom optimized ML operator libraries.\r\nResource Efficiency: Reducing the compute cost per video stream to enable massive scalability of our SaaS product.\r\nData Management: Building, improving, maintaining, and operating systems to facilitate the collection, labeling, and use of visual data for ML training.\r\nRequirements\r\nBS/MS or Ph.D. in Computer Science, Electrical Engineering, or a related discipline.\r\nStrong programming skills in C/C++ and Python .\r\nExperience with model optimization , quantization, and efficient deep learning techniques (e.g., knowledge distillation, pruning).\r\nDeep understanding of GPU hardware performance , including execution models, thread hierarchy, memory/cache management, and the cost/performance trade-offs of video processing.\r\nExperience with profiling and benchmarking tools (e.g., Nsight Systems, Nsight Compute) to validate performance on complex architectures.\r\nExperience identifying and resolving compute and data flow bottlenecks , particularly in high-bandwidth video processing pipelines.\r\nStrong communication skills and the ability to work cross-functionally between research and infrastructure teams.\r\nPreferred Qualifications\r\nFamiliarity with database systems (e.g., SQL, Neo4j).\r\nWork in Computer Vision , Deep Learning, and Vision Transformers.\r\nExperience with video processing frameworks such as NVIDIA DeepStream , DALI , or FFmpeg .\r\nFamiliarity with ML compilers (e.g., TVM, MLIR) or inference engines like TensorRT or ONNX Runtime.\r\nKnowledge of distributed training systems or cloud-scale inference serving (e.g., Triton Inference Server).\r\nJ-18808-Ljbffr","company":"Zensors","rawCompany":"zensors","city":"Millbrae","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-07-04T02:42:35.463Z","occupations":[{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"}],"industries":[{"code":"518210","title":"Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services","slug":"computing-infrastructure-providers-data-processing-web-hosting-and-related-services"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Machine Learning Infrastructure Engineer","description":"About Zensors\r\nZensors is the spatial intelligence platform for the physical world. Our AI platform provides real-time insights—from airport queue times to office utilization—helping organizations make smarter operational decisions.\r\nZensors is processing massive streams of video data 24/7 with human-level accuracy. To do this at scale, we rely on cutting-edge optimization to ensure our vision transformer and detection models run efficiently on both cloud and edge compute resources.\r\nResponsibility\r\nThe AI Infrastructure team at Zensors builds the engine that powers our visual sensing platform. We provide the tools to automate the lifecycle of our AI workflow, including model development, evaluation, optimization, deployment, and monitoring across thousands of video streams.\r\nAs a Machine Learning Engineer in ML Runtime & Optimization , you will develop technologies to accelerate the training and inference of computer vision models that power smart spaces and cities.\r\nYour responsibilities will include\r\nOptimizing Core ML Pipelines: Identifying key bottlenecks in our current video analytics pipeline and performing in-depth analysis to ensure the best possible performance on current server and edge compute architectures.\r\nCross-Stack Collaboration: Collaborating closely with AI research and platform engineering teams to optimize core parallel algorithms and influence the design of our next-generation inference infrastructure.\r\nModel Acceleration: Applying advanced model optimization techniques—such as quantization (Int8/FP16), pruning, and layer fusion—to our Vision Transformers (ViTs) and CNNs to maximize throughput and minimize latency.\r\nBuilding Efficient Operators: Working across the entire ML framework/compiler stack (e.g., PyTorch, CUDA, TensorRT, and NVIDIA DeepStream) to write custom optimized ML operator libraries.\r\nResource Efficiency: Reducing the compute cost per video stream to enable massive scalability of our SaaS product.\r\nData Management: Building, improving, maintaining, and operating systems to facilitate the collection, labeling, and use of visual data for ML training.\r\nRequirements\r\nBS/MS or Ph.D. in Computer Science, Electrical Engineering, or a related discipline.\r\nStrong programming skills in C/C++ and Python .\r\nExperience with model optimization , quantization, and efficient deep learning techniques (e.g., knowledge distillation, pruning).\r\nDeep understanding of GPU hardware performance , including execution models, thread hierarchy, memory/cache management, and the cost/performance trade-offs of video processing.\r\nExperience with profiling and benchmarking tools (e.g., Nsight Systems, Nsight Compute) to validate performance on complex architectures.\r\nExperience identifying and resolving compute and data flow bottlenecks , particularly in high-bandwidth video processing pipelines.\r\nStrong communication skills and the ability to work cross-functionally between research and infrastructure teams.\r\nPreferred Qualifications\r\nFamiliarity with database systems (e.g., SQL, Neo4j).\r\nWork in Computer Vision , Deep Learning, and Vision Transformers.\r\nExperience with video processing frameworks such as NVIDIA DeepStream , DALI , or FFmpeg .\r\nFamiliarity with ML compilers (e.g., TVM, MLIR) or inference engines like TensorRT or ONNX Runtime.\r\nKnowledge of distributed training systems or cloud-scale inference serving (e.g., Triton Inference Server).\r\nJ-18808-Ljbffr","datePosted":"2026-07-04T02:42:35.463Z","dateModified":"2026-07-04T02:42:35.463Z","hiringOrganization":{"@type":"Organization","name":"Zensors","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Millbrae","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"0f060019cfeca596ae761bd6"},"url":"https://jobsearcher.com/jobs/0f060019cfeca596ae761bd6"}}