{"schemaVersion":"jobsearcher.job.v1","id":"f567ac9f3959cf3148a209bf","url":"https://jobsearcher.com/jobs/f567ac9f3959cf3148a209bf","canonicalUrl":"https://jobsearcher.com/jobs/f567ac9f3959cf3148a209bf","title":"Lead Machine Learning Engineer","description":"Root is on a mission to unbreak insurance by creating experiences people love at prices they can’t believe. We believe that investing in world-class technology will facilitate a new class of insurance products, driving a massive positive impact on the hundreds of millions of drivers who carry auto insurance in the US. Root’s Engineering team is committed to building a flexible platform on which our product designers and quantitative scientists can quickly test ideas, deploy them into production, and iterate, with the ultimate objective of a delightful customer experience coupled with effective risk management.The OpportunityPrice is the most important component of an insurance product, with the ability to drive customer delight through lower prices unlocked by state-of-the-art predictive modeling. The Pricing Platform team owns the foundational technology that powers the R&D and production lifecycle for Root’s most critical machine learning models. This platform is a cornerstone of Root’s strategic goal of becoming the best in the world at pricing and automation.In this role, you will help build the next generation of Root’s machine learning platform for pricing, creating the infrastructure that allows researchers to move rapidly from experimentation to production. You will work closely with researchers on problems including feature pipelines and feature stores, reproducible model training and orchestration, model registries and versioning, automated validation, model serving, production observability, and tooling that ensures consistency between research and production. You will also explore how emerging LLM technology can revolutionize the data science workflow, improving the way models are developed, tested, deployed, and maintained, with the goal of dramatically reducing the time and effort required to turn new research into production pricing models.As a Lead Machine Learning Engineer, you are responsible for core team delivery and operations, turning the co-designed architecture into a reliable system through the team’s orchestration of the software development lifecycle. You will also ensure production systems are reliably serving the needs of our customers.This is a hands-on technical leadership role. You will write and review code, drive execution, and be accountable for the reliability of the systems your team owns.Salary Range: $164,200 - $240,000 (Eligible for competitive bonus and equity offering)Root is a \"work where it works best\" company. This means we will support you working in whatever location that works best for you across the US.How You Will Make An ImpactLead the implementation of the long-term technical roadmap that accelerates pricing innovation through ML tools and workflows that improve the end-to-end pricing R&D processWork closely with researchers to define platform needs that improve R&D ergonomics from data readiness through feature engineering, model fitting, serving, diagnostics, and monitoringContribute hands-on to core platform capabilities including feature pipelines and feature stores, reproducible model training and orchestration, model registries and versioning, automated validation, model serving, production observability, and supporting toolingAutomate end-to-end workflows, leveraging LLM technology to power agentic data science workflow automationOwn the execution of major platform capabilities by breaking down ambiguous problems, managing dependencies, guiding design decisions, and driving work from design through productionOrchestrate the software development lifecycle across the team and mentor and grow engineers through technical guidance and feedbackEnsure standards for reliability, observability, reproducibility, and correctness are met across the platform’s systemsWhat You Will Need To Succeed8+ years of software engineering experience, with a demonstrated track record of building and delivering production ML platforms and large-scale data processing systemsHands-on experience building core ML platform capabilities such as feature pipelines or feature stores, reproducible model training and orchestration, model registries and versioning, automated validation, or model servingStrong system design and distributed systems fundamentals, with experience building reliable, scalable production services and data pipelinesWorking knowledge of the ML lifecycle and the engineering considerations involved in training, evaluating, deploying, and operating models in productionExperience designing and operating systems with strong requirements around reliability, observability, reproducibility, and correctnessDemonstrated ability to take ambiguous technical initiatives, break them into executable work, manage dependencies, and drive delivery across multiple engineersStrong track record of working closely with Data Scientists or researchers to translate research needs into production platform capabilitiesExperience mentoring engineers, guiding technical design, and raising the engineering quality of a teamProficiency with Python and modern ML and data toolingExcellent communication skills with engineers, researchers, Product, and cross-functional stakeholdersPreferred QualificationsUnderstanding of ML models, including model inputs, assumptions, evaluation methodology, tradeoffs, and typical failure modes in productionExperience improving research velocity, experimentation throughput, deployment speed, or developer productivity through ML-platform investmentsExperience solving training-serving consistency, model lineage, reproducibility, or model-versioning challenges at scaleExperience building ML platforms in a regulated or highly data-intensive environment such as insurance, fintech, financial services, or healthcareExperience applying LLMs or agentic systems to developer tooling, research workflows, data science automation, or other internal productivity use casesAs part of Root's interview process, we kindly ask that all candidates be on camera for virtual interviews. This helps us create a more personal and engaging experience for both you and our interviewers. Being on camera is a standard requirement for our process and part of how we assess fit and communication style, so we do require it to move forward with any applicant's candidacy. If you have any concerns, feel free to let us know once you are contacted. We’re happy to talk it through.Please see our Privacy Notice available HERE for more information on how we process your personal data.Consistent with the Americans with Disabilities Act (ADA) and the Civil Rights Act of 1964, it is the policy of Root to provide reasonable accommodation when requested by a qualified applicant or candidate with a disability, unless such accommodation would cause an undue hardship for Root. The policy regarding requests for reasonable accommodation applies to all aspects of the hiring process. If reasonable accommodation is needed, please contact recruiting@joinroot.com.","company":"Root","rawCompany":"root","city":"Denver","state":"CO","isRemote":false,"isActive":false,"createdAt":"2026-09-01T08:42:08.293Z","occupations":[{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"}],"industries":[{"code":"524113","title":"Direct Life Insurance Carriers","slug":"direct-life-insurance-carriers"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"524126","title":"Direct Property and Casualty Insurance Carriers","slug":"direct-property-and-casualty-insurance-carriers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Lead Machine Learning Engineer","description":"Root is on a mission to unbreak insurance by creating experiences people love at prices they can’t believe. We believe that investing in world-class technology will facilitate a new class of insurance products, driving a massive positive impact on the hundreds of millions of drivers who carry auto insurance in the US. Root’s Engineering team is committed to building a flexible platform on which our product designers and quantitative scientists can quickly test ideas, deploy them into production, and iterate, with the ultimate objective of a delightful customer experience coupled with effective risk management.The OpportunityPrice is the most important component of an insurance product, with the ability to drive customer delight through lower prices unlocked by state-of-the-art predictive modeling. The Pricing Platform team owns the foundational technology that powers the R&D and production lifecycle for Root’s most critical machine learning models. This platform is a cornerstone of Root’s strategic goal of becoming the best in the world at pricing and automation.In this role, you will help build the next generation of Root’s machine learning platform for pricing, creating the infrastructure that allows researchers to move rapidly from experimentation to production. You will work closely with researchers on problems including feature pipelines and feature stores, reproducible model training and orchestration, model registries and versioning, automated validation, model serving, production observability, and tooling that ensures consistency between research and production. You will also explore how emerging LLM technology can revolutionize the data science workflow, improving the way models are developed, tested, deployed, and maintained, with the goal of dramatically reducing the time and effort required to turn new research into production pricing models.As a Lead Machine Learning Engineer, you are responsible for core team delivery and operations, turning the co-designed architecture into a reliable system through the team’s orchestration of the software development lifecycle. You will also ensure production systems are reliably serving the needs of our customers.This is a hands-on technical leadership role. You will write and review code, drive execution, and be accountable for the reliability of the systems your team owns.Salary Range: $164,200 - $240,000 (Eligible for competitive bonus and equity offering)Root is a \"work where it works best\" company. This means we will support you working in whatever location that works best for you across the US.How You Will Make An ImpactLead the implementation of the long-term technical roadmap that accelerates pricing innovation through ML tools and workflows that improve the end-to-end pricing R&D processWork closely with researchers to define platform needs that improve R&D ergonomics from data readiness through feature engineering, model fitting, serving, diagnostics, and monitoringContribute hands-on to core platform capabilities including feature pipelines and feature stores, reproducible model training and orchestration, model registries and versioning, automated validation, model serving, production observability, and supporting toolingAutomate end-to-end workflows, leveraging LLM technology to power agentic data science workflow automationOwn the execution of major platform capabilities by breaking down ambiguous problems, managing dependencies, guiding design decisions, and driving work from design through productionOrchestrate the software development lifecycle across the team and mentor and grow engineers through technical guidance and feedbackEnsure standards for reliability, observability, reproducibility, and correctness are met across the platform’s systemsWhat You Will Need To Succeed8+ years of software engineering experience, with a demonstrated track record of building and delivering production ML platforms and large-scale data processing systemsHands-on experience building core ML platform capabilities such as feature pipelines or feature stores, reproducible model training and orchestration, model registries and versioning, automated validation, or model servingStrong system design and distributed systems fundamentals, with experience building reliable, scalable production services and data pipelinesWorking knowledge of the ML lifecycle and the engineering considerations involved in training, evaluating, deploying, and operating models in productionExperience designing and operating systems with strong requirements around reliability, observability, reproducibility, and correctnessDemonstrated ability to take ambiguous technical initiatives, break them into executable work, manage dependencies, and drive delivery across multiple engineersStrong track record of working closely with Data Scientists or researchers to translate research needs into production platform capabilitiesExperience mentoring engineers, guiding technical design, and raising the engineering quality of a teamProficiency with Python and modern ML and data toolingExcellent communication skills with engineers, researchers, Product, and cross-functional stakeholdersPreferred QualificationsUnderstanding of ML models, including model inputs, assumptions, evaluation methodology, tradeoffs, and typical failure modes in productionExperience improving research velocity, experimentation throughput, deployment speed, or developer productivity through ML-platform investmentsExperience solving training-serving consistency, model lineage, reproducibility, or model-versioning challenges at scaleExperience building ML platforms in a regulated or highly data-intensive environment such as insurance, fintech, financial services, or healthcareExperience applying LLMs or agentic systems to developer tooling, research workflows, data science automation, or other internal productivity use casesAs part of Root's interview process, we kindly ask that all candidates be on camera for virtual interviews. This helps us create a more personal and engaging experience for both you and our interviewers. Being on camera is a standard requirement for our process and part of how we assess fit and communication style, so we do require it to move forward with any applicant's candidacy. If you have any concerns, feel free to let us know once you are contacted. We’re happy to talk it through.Please see our Privacy Notice available HERE for more information on how we process your personal data.Consistent with the Americans with Disabilities Act (ADA) and the Civil Rights Act of 1964, it is the policy of Root to provide reasonable accommodation when requested by a qualified applicant or candidate with a disability, unless such accommodation would cause an undue hardship for Root. The policy regarding requests for reasonable accommodation applies to all aspects of the hiring process. If reasonable accommodation is needed, please contact recruiting@joinroot.com.","datePosted":"2026-09-01T08:42:08.293Z","dateModified":"2026-09-01T08:42:08.293Z","hiringOrganization":{"@type":"Organization","name":"Root","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Denver","addressRegion":"CO","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"f567ac9f3959cf3148a209bf"},"url":"https://jobsearcher.com/jobs/f567ac9f3959cf3148a209bf"}}