Top Machine Learning Jobs in Atlanta, GA

As a Senior Staff Machine Learning Engineer, you will design and implement machine learning algorithms and systems, collaborate with teams to integrate AI into applications, develop RESTful APIs, and manage version control and CI/CD pipelines, while ensuring compliance in regulated environments.

As a Senior AI-Machine Learning Engineer at Abbott, you will lead the development of innovative machine learning features for a new device and cloud platform in the Structural Heart division, requiring technical leadership and proficiency in AI/ML programming languages and methodologies.

The Senior AI/ML Engineer will design, implement, and maintain machine learning algorithms and pipelines, collaborate with the AI platform and engineering teams, manage version control with GitHub, and develop RESTful APIs. They will work in a regulated industry, advocating for team decisions and balancing speed with compliance requirements.

As a Lead Machine Learning Engineer, you will develop advanced analytics and machine learning solutions, manage projects from conception to operationalization, and provide technical guidance. You'll collaborate with a global AI/ML team, designing scalable applications and contributing to core analytics platforms.

The Senior Machine Learning Engineer will design, implement, and maintain machine learning algorithms and pipelines. Collaborating closely with AI platform and engineering teams, the role involves integrating AI models into applications, managing version control with GitHub, and ensuring smooth API operations in a regulatory environment.

The Machine Learning Engineer will integrate AI hardware with Hugging Face's transformers models, ensuring performance standards are met and creating user-friendly Python APIs. The role involves low-level system integration, hardware collaboration, and developing efficient deployment strategies.

As a Senior Machine Learning Engineer, you will design and implement machine learning algorithms, analyze structured and unstructured data, and improve product/system performance. Responsibilities include developing coding solutions, performing testing and debugging, applying deep learning technologies, and collaborating across teams.

The R&D AI Software Engineer will design and implement end-to-end machine learning projects, improve AI pipelines, and contribute to high-quality production code. The role involves working with live data streams, developing benchmark tasks, and enhancing LLM accuracy through innovative data preprocessing techniques.

As a Machine Learning Software Engineer, you'll build and improve ML-powered systems, propose technical architectures, design and manage the ML pipeline, and ensure effective communication in a remote environment. Your role will focus on delivering real business impact through scalable machine learning solutions.

The Machine Learning Researcher/Engineer will conduct research on foundational models, perform distributed model training, enhance existing architectures, design experiments, and potentially oversee data preparation activities. This role plays a critical part in a high-impact, ambitious research project within a collaborative and innovative environment.

The Staff Machine Learning Engineer will design, implement, and maintain machine learning algorithms and pipelines, integrate AI components into systems, and collaborate across teams using CI/CD pipelines and RESTful APIs. The role focuses on rapid deployment in regulated environments and necessitates effective communication with stakeholders.

As a Lead Machine Learning Engineer, you will lead the development and deployment of machine learning solutions, oversee model pipelines, ensure application scalability and performance, collaborate across teams, and mentor peers. You'll also enforce standards and best practices while driving innovation in AI solutions.

The Machine Learning Engineer will design, develop, and maintain scalable machine learning systems and data pipelines. Responsibilities include collaborating with data engineers and scientists, writing production-quality code, troubleshooting ML model issues, and deploying data science solutions to enhance business value.

As a Machine Learning Engineer at Tiger Analytics, you will create scalable machine learning systems, manage end-to-end data and model pipelines, and ensure high-quality deployment of data science solutions in collaboration with business partners and cross-functional teams.

The Machine Learning Engineer/Researcher will design and implement machine learning algorithms to enhance security detection and integration capabilities of Stellar Cyber’s Open XDR platform. Responsibilities include collaborating with data integration and security teams, translating use cases into machine learning tasks, and monitoring the efficacy of detection solutions.

As an ML Solutions Architect, you will design and implement machine learning solutions for the Conversation AI platform. Your role includes developing scalable ML architectures, end-to-end ML pipelines, and integrating the latest advancements in ML technologies. You will collaborate with cross-functional teams to address business challenges and drive innovative solutions.

The Machine Learning Engineer will design scalable systems for deploying and validating data science solutions, create production-quality code, and build reusable data pipelines. Collaboration with data engineers and scientists is essential to manage infrastructure and troubleshoot ML models, ensuring high-performance solutions.