{"schemaVersion":"jobsearcher.job.v1","id":"6e07da9b59d7d6cbcb55dc3e","url":"https://jobsearcher.com/jobs/6e07da9b59d7d6cbcb55dc3e","canonicalUrl":"https://jobsearcher.com/jobs/6e07da9b59d7d6cbcb55dc3e","title":"Machine Learning Engineer","description":"About The RoleThe role owns the architecture, development, and scaling of machine learning systems, driving the transition of advanced AI models from research into high-throughput production environments.The team collaborates closely with applied scientists and backend engineers to ensure models achieve optimal performance, low latency, and robust reliability under heavy enterprise workloads.Key ResponsibilitiesDesign and implement scalable machine learning pipelines for model training, validation, and inference using Python, PyTorch, and distributed computing frameworksDeploy, monitor, and scale models on cloud platforms like AWS SageMaker or GCP Vertex AI with automated CI/CD pipelinesOptimize model inference latency, throughput, and memory footprint through quantization, pruning, and hardware acceleration techniquesBuild feature and data ingestion pipelines handling large-scale datasets, ensuring consistency between training and production feature storesImplement comprehensive monitoring frameworks to track model performance, data drift, and anomaly detection in real-time production environmentsWrite rigorous unit and integration tests, conduct code reviews, and establish engineering best practices for the broader machine learning teamWhat We Are Looking For3-6 years of professional software and machine learning engineering experience with a track record of deploying models to productionStrong proficiency in Python and hands-on experience with deep learning frameworks such as PyTorch or TensorFlowSolid understanding of MLOps best practices, containerization with Docker, and orchestration using KubernetesExperience with cloud infrastructure (AWS, GCP, or Azure) and modern feature stores or vector databasesBachelor's or Master's degree in Computer Science, Machine Learning, Statistics, or a related quantitative fieldBonus: Experience fine-tuning large language models, contributing to open-source ML projects, or publishing research at top-tier AI conferences","company":"Evlo Ai","rawCompany":"evlo ai","city":"Washington","state":"DC","isRemote":false,"isActive":false,"createdAt":"2026-08-08T11:46:05.834Z","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":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Machine Learning Engineer","description":"About The RoleThe role owns the architecture, development, and scaling of machine learning systems, driving the transition of advanced AI models from research into high-throughput production environments.The team collaborates closely with applied scientists and backend engineers to ensure models achieve optimal performance, low latency, and robust reliability under heavy enterprise workloads.Key ResponsibilitiesDesign and implement scalable machine learning pipelines for model training, validation, and inference using Python, PyTorch, and distributed computing frameworksDeploy, monitor, and scale models on cloud platforms like AWS SageMaker or GCP Vertex AI with automated CI/CD pipelinesOptimize model inference latency, throughput, and memory footprint through quantization, pruning, and hardware acceleration techniquesBuild feature and data ingestion pipelines handling large-scale datasets, ensuring consistency between training and production feature storesImplement comprehensive monitoring frameworks to track model performance, data drift, and anomaly detection in real-time production environmentsWrite rigorous unit and integration tests, conduct code reviews, and establish engineering best practices for the broader machine learning teamWhat We Are Looking For3-6 years of professional software and machine learning engineering experience with a track record of deploying models to productionStrong proficiency in Python and hands-on experience with deep learning frameworks such as PyTorch or TensorFlowSolid understanding of MLOps best practices, containerization with Docker, and orchestration using KubernetesExperience with cloud infrastructure (AWS, GCP, or Azure) and modern feature stores or vector databasesBachelor's or Master's degree in Computer Science, Machine Learning, Statistics, or a related quantitative fieldBonus: Experience fine-tuning large language models, contributing to open-source ML projects, or publishing research at top-tier AI conferences","datePosted":"2026-08-08T11:46:05.834Z","dateModified":"2026-08-08T11:46:05.834Z","hiringOrganization":{"@type":"Organization","name":"Evlo Ai","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Washington","addressRegion":"DC","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"6e07da9b59d7d6cbcb55dc3e"},"url":"https://jobsearcher.com/jobs/6e07da9b59d7d6cbcb55dc3e"}}