{"schemaVersion":"jobsearcher.job.v1","id":"cfbf645e4fc6b600ffb3cd0b","url":"https://jobsearcher.com/jobs/cfbf645e4fc6b600ffb3cd0b","canonicalUrl":"https://jobsearcher.com/jobs/cfbf645e4fc6b600ffb3cd0b","title":"Site Reliability Engineer","description":"Overview\nIn this role you architect AI-native production pipelines and agentic ML systems at Trimble, shaping how AI agents operate within our SaaS platform. You’ll bridge reliability with ML orchestration, driving Agent Ops and scalable MLOps practices. Expect to translate ambiguous business needs into concrete ML tasks, while ensuring performance, cost-efficiency, and resilience. This is a hands-on, entrepreneurial role that impacts how we deploy intelligent capabilities at scale.\n\nCompensation / BenefitsMedicalDentalVisionLifeDisabilityTime off plans\nResponsibilitiesArchitect and orchestrate agentic behaviors and ML models from conception to production deploymentAdvance MLOps and Agent Ops practices including containerization, versioning, and monitoring for rigorCollaborate with domain experts to convert business needs into robust ML solutionsResearch and adapt frontier ML approaches (foundation models, self-supervised methods) into production codeTest, debug, and optimize ML models for efficiency, scalability, and reliability in complex environments\nKey requirements3+ years of relevant experience with a Bachelor’s degree in CS/Engineering or a quantitative field (or 1–3 years with a Master’s)Strong knowledge of machine learning and deep learning principles (model selection, training, evaluation)Fluency in Python and PyTorch; familiarity with NumPy, pandas, scikit-learnTrack record of shipping production-ready ML work; solid GitHub portfolio or open-source contributionsSolid grounding in data structures, software architecture, Linux, and bash scriptingCollaborative mindsetEntrepreneurial problem-solving in ambiguityStrong communication and cross-functional teamworkMLOps and Agent Ops practicesContainerization, model versioning, robust monitoringExperience with foundation models and self-supervised methods","company":"Trimble Navigation","rawCompany":"trimble navigation","city":"Aurora","state":"CO","isRemote":false,"isActive":false,"createdAt":"2026-09-15T04:37:54.472Z","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":"15-2051.00","title":"Data Scientists","slug":"data-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":"541519","title":"Other Computer Related Services","slug":"other-computer-related-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Site Reliability Engineer","description":"Overview\nIn this role you architect AI-native production pipelines and agentic ML systems at Trimble, shaping how AI agents operate within our SaaS platform. You’ll bridge reliability with ML orchestration, driving Agent Ops and scalable MLOps practices. Expect to translate ambiguous business needs into concrete ML tasks, while ensuring performance, cost-efficiency, and resilience. This is a hands-on, entrepreneurial role that impacts how we deploy intelligent capabilities at scale.\n\nCompensation / BenefitsMedicalDentalVisionLifeDisabilityTime off plans\nResponsibilitiesArchitect and orchestrate agentic behaviors and ML models from conception to production deploymentAdvance MLOps and Agent Ops practices including containerization, versioning, and monitoring for rigorCollaborate with domain experts to convert business needs into robust ML solutionsResearch and adapt frontier ML approaches (foundation models, self-supervised methods) into production codeTest, debug, and optimize ML models for efficiency, scalability, and reliability in complex environments\nKey requirements3+ years of relevant experience with a Bachelor’s degree in CS/Engineering or a quantitative field (or 1–3 years with a Master’s)Strong knowledge of machine learning and deep learning principles (model selection, training, evaluation)Fluency in Python and PyTorch; familiarity with NumPy, pandas, scikit-learnTrack record of shipping production-ready ML work; solid GitHub portfolio or open-source contributionsSolid grounding in data structures, software architecture, Linux, and bash scriptingCollaborative mindsetEntrepreneurial problem-solving in ambiguityStrong communication and cross-functional teamworkMLOps and Agent Ops practicesContainerization, model versioning, robust monitoringExperience with foundation models and self-supervised methods","datePosted":"2026-09-15T04:37:54.472Z","dateModified":"2026-09-15T04:37:54.472Z","hiringOrganization":{"@type":"Organization","name":"Trimble Navigation","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Aurora","addressRegion":"CO","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"cfbf645e4fc6b600ffb3cd0b"},"url":"https://jobsearcher.com/jobs/cfbf645e4fc6b600ffb3cd0b"}}