Top Remote Machine Learning Jobs in Atlanta, GA

Design and implement the core Risk platform architecture, manage projects, mentor team members, and write high quality code for Coinbase products.

Lead the design, development, and deployment of machine learning models, collaborating with teams and mentoring engineers. Focus on scalable ML workflows.

The Principal ML Engineer at Agero will develop and productionize a Dispatch System using ML models, optimize decision-making processes, and lead a team to improve operational performance.

The AI Engagement Manager will foster relationships with top partners, facilitate adoption of AI solutions, and analyze performance using BI tools.

Lead the product vision, strategy, and execution for Consumer Risk within Checkout at Affirm, focusing on credit decisioning, pricing, and fraud prevention while managing a team of Product Managers and collaborating with various functional teams.

Join Wasabi as an AI Engineer intern to build scalable machine learning solutions. Responsibilities include maintaining ML pipelines, model integration, and collaboration with various teams to drive AI initiatives.

The Engineering Manager will lead a team of Data Scientists and ML Engineers to build a Dispatch Optimization platform, focusing on model deployment and operational excellence.

As a Business Operations Associate, you will support data-driven decisions and business initiatives, lead experiments, and optimize operational efficiency at Jerry.ai.

The Principal Product Manager will drive the evolution of AI-enabled products, collaborate with C-level leadership, and ensure customer-centric innovation while managing product direction from concept to market.

As Principal Product Manager for the Agentic Platform, you will set the vision and strategy for AI infrastructure, drive product development, and facilitate adoption across teams.

Lead research on large language models to address healthcare challenges, collaborating with teams to enhance model performance and ensure real-world applicability.

As a Machine Learning Engineer, you will perform analysis on healthcare data, develop and deploy clinical machine learning models, and collaborate with various stakeholders.

Design, develop, and deliver machine learning models for clinical data. Collaborate with stakeholders and optimize production ML systems.

Lead development of a greenfield data product by partnering with customers and product teams to build analytics and models (simulation, Bayesian networks, optimization, ML). Maintain existing analytics, improve predictive churn models, and translate customer needs into production-grade SQL/dbt pipelines, dashboards, and data-driven product features.

As a Senior Machine Learning Engineer, you'll design and manage ML infrastructure, work with Data Science to implement models, and oversee system health and reliability.

Build and optimize machine learning models for 3D dental applications, research state-of-the-art methods, and implement MLOps best practices.

Lead and manage a team of Regional Sales Managers, focusing on talent acquisition and exceeding sales goals in the enterprise sector.

The Principal AI/ML Engineer will develop machine learning models for campaign optimization in AdTech, collaborating across engineering and product teams to enhance advertising capabilities.

The engineer will build and operate AI/ML infrastructure, managing services on AWS and bare metal, using tools like Kubernetes and Terraform.

The Technical Marketing Manager will oversee marketing for AppSec, AI/ML Security, and Research & Engineering, focusing on content creation, social media, demand generation, and analytics. This role requires technical fluency and collaboration with engineering teams to effectively communicate complex concepts to technical and business audiences.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

As a Systems Architect, you'll define and drive the infrastructure architecture for AI/ML workloads, design multi-cloud strategies, manage compute orchestration and storage systems, and lead capacity planning, while also collaborating with engineering leadership.

As an ML Ops Infrastructure Engineer, you will design CI/CD pipelines for ML, maintain deployment systems, and implement monitoring while collaborating with research teams to ensure model quality and performance.

Design, implement, and optimize scaled lifecycle campaigns across email and in-product channels to drive activation, revenue, retention, and cross-sell. Use marketing automation, behavioral data, and ML techniques to personalize experiences, analyze performance, and collaborate across growth, product, sales, and data teams.

The role involves researching, developing, and implementing AI solutions, optimizing models, integrating AI into workflows, and collaborating with teams to enhance business efficiency.