Top Machine Learning Jobs in Atlanta, GA

As an AI Tutor, you will teach, evaluate, and red-team AI models in data science principles, focusing on statistical modeling, machine learning, and data ethics.

The Data Engineer will design and maintain ETL pipelines and data storage solutions, collaborating with cross-functional teams to ensure efficient data processing and delivery.

Manage a team of data scientists and analytics engineers to innovate Netflix's core product experience, driving measurement and insights across the TV and interactive discovery domains.

Lead Reliability & Observability initiatives, collaborate with engineering and product teams, ensure robust operations of Airbnb's platform, and drive AI projects.

Lead the development of first-party measurement solutions, integrate with third-party partners, and ensure high-quality analytics outputs within the ads ecosystem.

The Senior AI Engineer will develop AI solutions for incident management, collaborate cross-functionally, and enhance observability tools through iteration and ownership.

The Software Engineer Manager, Machine Learning will lead a team, drive machine learning projects, support engineering talent development, and ensure collaboration with product teams.

The Machine Learning Engineer will fine-tune and deploy Large Language Models, optimize ML models, maintain data pipelines, and collaborate on AI features.

The ML Platform Engineer will develop and scale ML infrastructure, automate workflows, and collaborate with data scientists to deploy models efficiently in production.

As a Senior Machine Learning Engineer, you'll design and implement ML models, develop data pipelines, and collaborate on AI features at Mural.

Develop and optimize machine learning models for financial and operational workflows at Netflix, ensuring model reliability and integration with studio applications.

The Staff ML/AI Platform Engineer will design, prototype and deploy AI/ML models, optimize pipelines, mentor junior engineers, and collaborate closely with other teams to develop machine learning solutions.

The role involves building and maintaining ML pipelines, optimizing workflows, and contributing to product strategy with a focus on usability.

Lead the definition and rollout of AI-powered product features, collaborating with teams to enhance fraud prevention solutions.

The Director of Artificial Intelligence will define, lead, and implement AI strategies, managing R&D, building teams, and ensuring ethical AI use to drive business outcomes.

Lead the design, execution, and analysis of machine learning-driven product experiments to optimize ordering systems. Collaborate with stakeholders and mentor teams.

Design and implement production-grade ML systems for ICS/OT cybersecurity, build and optimize models (including NLP/LLMs), develop data pipelines and MLOps workflows, collaborate with data teams to deploy and monitor models in cloud and on-prem containerized environments, and troubleshoot production performance and resource issues.

Lead design, development, training, optimization, and production deployment of generative AI models (text, image, multimodal). Prepare and manage large datasets, run experiments and prototypes, evaluate performance, collaborate with cross-functional teams, and scale models for reliable production use.

Design and optimize machine learning pipelines for large-scale systems, integrating models into user features and ensuring data integrity and performance.

Lead the design and optimization of ML systems at Scribd, managing cross-team projects, mentoring engineers, and shaping technical direction for ML initiatives.

Design and optimize machine learning systems, build scalable data pipelines, and run experiments to improve recommendations for Scribd's products.

Lead and mentor ML and data engineering teams to build scalable, production ML models and data pipelines for risk detection. Partner with product and cross-functional teams, ensure CI/CD, monitoring, reliability, on-call processes, and prioritize tactical and strategic roadmaps to mitigate fraud and compliance risk.

The Applied Scientist AI/ML Intern will work on building data pipelines, finetuning LLMs, and deploying AI models in a collaborative team environment.

As an AI Applications Engineer, you will bridge AI research and implementation, driving experiments, building infrastructure, and releasing products to improve user experience and metrics.

This role involves leading a perception team in developing algorithms for autonomous driving systems, supervising team members, and conducting research to enhance perception capabilities.