285 Machine Learning Engineer jobs in United States available for remote work from home. Apply for positions such as Machine Learning Engineer, Machine Learning Engineer, Machine Learning Engineer and more! Discover the best work-from-home or hybrid, full- and part-time jobs.
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The Machine Learning Engineer will design, build, and deploy production-grade machine learning models and AI systems within the ADT ecosystem. This role involves operationalizing algorithms, automating retraining cycles, and collaborating with cross-functional teams to integrate ML capabilities into customer-facing applications.
Design, deploy, and maintain production-grade machine learning models using healthcare and pharmacy data to optimize patient outcomes and operations. Collaborate with cross-functional teams to implement MLOps best practices and ensure compliance with healthcare regulations.
The Machine Learning Engineer will collaborate with cross-functional teams to develop and deploy machine learning solutions that support clinical and operational outcomes. Responsibilities include managing the end-to-end lifecycle of AI projects, from data engineering and model training to deployment and monitoring.
The Machine Learning Engineer will lead the design, development, and deployment of scalable machine learning models to support various business functions. They will collaborate with cross-functional teams to build production-ready solutions and ensure robust model governance and MLOps practices.
You will own machine learning products end-to-end, from problem discovery and design to production deployment and performance evaluation. Responsibilities include building scalable data pipelines, developing retrieval and ranking systems, and implementing agentic LLM workflows.
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You will own the end-to-end research loop, from raw data and desk ideas to building, training, and validating deep-learning models. You are responsible for managing your own compute infrastructure and ensuring models are effectively integrated into daily trading operations.
You will design, develop, and optimize computer vision models and deep learning capabilities across the product portfolio. Additionally, you will manage the end-to-end machine learning lifecycle, including data collection, annotation, model training, and production deployment.
You will own the full lifecycle of ML model development, from exploratory analysis and design to production deployment and monitoring. You will collaborate with engineering and cross-functional teams to translate business objectives into reliable, data-driven forecasting solutions.
The Machine Learning Engineer will support major customer projects by building and maintaining ML pipelines and real-time inference services. The role focuses on creating software services using existing models rather than conducting academic research.
The role involves fine-tuning transformer-based models and implementing real-time production pipelines using LLMs and multimodal models. Responsibilities include building evaluation frameworks, deploying models at scale, and collaborating with cross-functional teams to deliver intelligent solutions.
Build statistical models and production systems to optimize advertiser performance and business goals within the mobile ad tech marketplace. Design and execute experiments to validate economic theories and develop applications for budget retention and bidding innovation.
Develop and deploy end-to-end agentic, full-stack AI systems for defense and intelligence customers. Build ML services using LLMs, embeddings, and RAG within production and air-gapped environments.
Build, train, and improve production-ready machine learning models while developing evaluation and feedback loops. Collaborate cross-functionally to implement ML-powered capabilities and establish tooling for monitoring model quality.
The role involves preparing datasets, training and optimizing ML models, and maintaining inference services to improve search and retrieval quality. You will collaborate with cross-functional teams to build AI systems for ranking and recommendations, including LLM workflows.
The role involves designing, developing, and deploying scalable machine learning models to solve real-world problems. The engineer will collaborate with cross-functional teams to integrate these models into production systems and optimize them for performance.
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Design and build scalable ML data pipelines and translate R&D outputs into reusable libraries and products. Collaborate with customers and simulation engineers to embed AI models into practical engineering tools.
Design, train, and deploy machine learning models across multimodal domains for safety, security, and intelligence applications. Build agentic systems and evaluation pipelines to measure model reliability and safety in production environments.
Own the full machine learning lifecycle from problem discovery and design to deployment and monitoring of ML-powered features. Collaborate with cross-functional teams to implement models that improve customer experience and business growth.
Develop and deliver end-to-end machine learning solutions, encompassing technical requirement definition, scalable system architecture, and implementing monitoring and maintenance workflows. Collaborate with cross-functional teams to build new ML products and lead the design and implementation of MLOps frameworks.
Train, fine-tune, and deploy generative models across multiple modalities while optimizing model inference performance. Collaborate closely with product and engineering teams to implement AI features in the application.
Build and maintain data pipelines for machine learning models and contribute to infrastructure supporting distributed training workflows. Collaborate with engineers to improve the reliability, scalability, and efficiency of the offline ML platform.
You will own the development and deployment of speech and natural language models for autonomous systems operating in resource-constrained tactical environments. This involves building data pipelines, optimizing model latency and footprint, and maturing research code into production-ready inference paths.
Build and improve machine learning models and data-driven systems to classify, label, and enrich vast amounts of Internet telemetry data. Partner with cross-functional teams to design applied ML workflows that transform raw data into actionable insights for internal systems and customer products.
You will define and drive the machine learning engineering strategy for Discovery Mode and related royalty programs while designing and shipping scalable production systems. You will also collaborate with cross-functional teams to build product capabilities that strengthen connections between artists and fans.
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You will architect and own end-to-end AI solutions at global scale, including LLM-powered workflows and real-time fraud defenses. You will also collaborate with cross-functional teams to design, deploy, and monitor production AI systems while mentoring teammates.
The role involves evaluating complex machine learning scenarios and developing predictive models to address real-world business problems. You will also write and optimize code while providing expert guidance on analytical workflows and technical documentation.
The role involves evaluating complex machine learning scenarios and developing predictive models to address real-world business problems. You will also be responsible for writing and optimizing code while providing expert guidance on analytical workflows and model validation.
The Machine Learning Engineering Fellow will architect and drive innovative machine learning solutions across search and recommendation platforms while providing technical leadership. They will also mentor engineering staff and partner with senior leadership to influence long-term technical strategy.
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