{"schemaVersion":"jobsearcher.job.v1","id":"9968a74bc383fda5bbba4f86","url":"https://jobsearcher.com/jobs/9968a74bc383fda5bbba4f86","canonicalUrl":"https://jobsearcher.com/jobs/9968a74bc383fda5bbba4f86","title":"Staff Machine Learning Engineer","description":"Job Title\r\nStaff Machine Learning Engineer\r\nLocation\r\nSan Jose, CA\r\nEmployment Type\r\nFull Time role\r\nClient\r\n100% Onsite role\r\nRole Description\r\nThis is a \"full-stack\" ML systems role for a senior individual contributor and technical architect. You will be responsible for designing the complete ML ecosystem for our edge devices, from the cloud-native MLOps platform down to the bare-metal model optimization.\r\nThis unique role blends three key domains: MLOps & Data; Agentic & Edge AI; Systems & Hardware.\r\nYou are the engineer who will not only build our ML platform but also design the intelligent agents it deploys and ensure they run faster and more securely than anyone else's.\r\nKey Responsibilities\r\nArchitecture & Leadership\r\nAct as a senior individual contributor, leading by example with hands-on coding, design, and analysis across the entire ML stack.\r\nDefine the end-to-end architecture for our MLOps, agentic AI, and model optimization strategy.\r\nMLOps & Data Platform\r\nDesign and implement our data processing and versioning pipelines, ensuring data integrity and traceability.\r\nBuild the infrastructure for our Human-in-the-Loop (HITL) and AI-in-the-Loop (Active Learning) data labeling systems to continuously improve our datasets.\r\nDevelop a comprehensive lightweight on-device monitoring system to track not just operational metrics but also inference quality and concept drift.\r\nAgentic & Edge Development\r\nDesign and develop autonomous agents that operate on our resource-constrained edge devices.\r\nIntegrate deep domain knowledge, including real-time log analysis, computer vision, and interaction with open-source system tools.\r\nSecurity & Optimization\r\nDefine and implement the complete security and verification framework for our edge models. This includes MCP/A2A-like secure protocols, MCP authentication, entity verification (e.g., model signing), and model injection prevention.\r\nServe as the primary technical bridge to our silicon teams. Collaborate with RTL designers to influence future NPU and FPGA architecture from an ML software perspective.\r\nLead R&D on model optimization for our specific AI inference engine, applying both graph-level (e.g., operator fusion) and OP-level (e.g., custom ops) techniques.\r\nQualifications\r\n8–10+ years of hands-on experience in machine learning, with a proven track record as a senior or staff-level individual contributor.\r\nPh.D. or M.S. in Computer Science, Electrical Engineering, or a related field (or equivalent practical experience).\r\nExpert-level programming in Python and deep experience with ML frameworks (e.g., PyTorch, TensorFlow).\r\nDeep theoretical understanding of modern ML algorithms (e.g., Transformers).\r\nA strong foundational understanding of computer architecture, digital logic, and the role of RTL (Verilog/VHDL) in the hardware design lifecycle.\r\nProven experience architecting and building end-to-end MLOps lifecycles, from data ingestion to production monitoring and labeling loops.\r\nProven experience developing agentic systems or applications using LLMs.\r\nDemonstrable domain knowledge in log analysis and/or computer vision.\r\nExperience with on-device model security (verification, anti-injection) and secure communication protocols.\r\nHands-on experience optimizing models for hardware (NPUs, GPUs) at graph and operator levels.\r\nJ-18808-Ljbffr","company":"Technologies","rawCompany":"technologies","city":"San Jose","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-06-25T01:21:30.245Z","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":"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":"Staff Machine Learning Engineer","description":"Job Title\r\nStaff Machine Learning Engineer\r\nLocation\r\nSan Jose, CA\r\nEmployment Type\r\nFull Time role\r\nClient\r\n100% Onsite role\r\nRole Description\r\nThis is a \"full-stack\" ML systems role for a senior individual contributor and technical architect. You will be responsible for designing the complete ML ecosystem for our edge devices, from the cloud-native MLOps platform down to the bare-metal model optimization.\r\nThis unique role blends three key domains: MLOps & Data; Agentic & Edge AI; Systems & Hardware.\r\nYou are the engineer who will not only build our ML platform but also design the intelligent agents it deploys and ensure they run faster and more securely than anyone else's.\r\nKey Responsibilities\r\nArchitecture & Leadership\r\nAct as a senior individual contributor, leading by example with hands-on coding, design, and analysis across the entire ML stack.\r\nDefine the end-to-end architecture for our MLOps, agentic AI, and model optimization strategy.\r\nMLOps & Data Platform\r\nDesign and implement our data processing and versioning pipelines, ensuring data integrity and traceability.\r\nBuild the infrastructure for our Human-in-the-Loop (HITL) and AI-in-the-Loop (Active Learning) data labeling systems to continuously improve our datasets.\r\nDevelop a comprehensive lightweight on-device monitoring system to track not just operational metrics but also inference quality and concept drift.\r\nAgentic & Edge Development\r\nDesign and develop autonomous agents that operate on our resource-constrained edge devices.\r\nIntegrate deep domain knowledge, including real-time log analysis, computer vision, and interaction with open-source system tools.\r\nSecurity & Optimization\r\nDefine and implement the complete security and verification framework for our edge models. This includes MCP/A2A-like secure protocols, MCP authentication, entity verification (e.g., model signing), and model injection prevention.\r\nServe as the primary technical bridge to our silicon teams. Collaborate with RTL designers to influence future NPU and FPGA architecture from an ML software perspective.\r\nLead R&D on model optimization for our specific AI inference engine, applying both graph-level (e.g., operator fusion) and OP-level (e.g., custom ops) techniques.\r\nQualifications\r\n8–10+ years of hands-on experience in machine learning, with a proven track record as a senior or staff-level individual contributor.\r\nPh.D. or M.S. in Computer Science, Electrical Engineering, or a related field (or equivalent practical experience).\r\nExpert-level programming in Python and deep experience with ML frameworks (e.g., PyTorch, TensorFlow).\r\nDeep theoretical understanding of modern ML algorithms (e.g., Transformers).\r\nA strong foundational understanding of computer architecture, digital logic, and the role of RTL (Verilog/VHDL) in the hardware design lifecycle.\r\nProven experience architecting and building end-to-end MLOps lifecycles, from data ingestion to production monitoring and labeling loops.\r\nProven experience developing agentic systems or applications using LLMs.\r\nDemonstrable domain knowledge in log analysis and/or computer vision.\r\nExperience with on-device model security (verification, anti-injection) and secure communication protocols.\r\nHands-on experience optimizing models for hardware (NPUs, GPUs) at graph and operator levels.\r\nJ-18808-Ljbffr","datePosted":"2026-06-25T01:21:30.245Z","dateModified":"2026-06-25T01:21:30.245Z","hiringOrganization":{"@type":"Organization","name":"Technologies","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Jose","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"9968a74bc383fda5bbba4f86"},"url":"https://jobsearcher.com/jobs/9968a74bc383fda5bbba4f86"}}