Top Machine Learning Jobs in Atlanta, GA

Lead full-cycle enterprise sales into Fortune 500 retailers, marketplaces, and travel companies. Manage eight-figure deals with C-suite and technical buyers, coordinate internal resources, build procurement-ready ROI/business cases, run pilots/A-B tests, and drive land-and-expand growth while shaping GTM playbooks.

Design, build, and scale low-latency distributed platform components; define technical vision and architecture; collaborate with product and ML teams; evaluate build-vs-buy decisions; prototype research breakthroughs and deliver robust, production-grade services.

Lead Martech technology strategy and execution for marketing and loyalty domains. Define AI/ML-driven roadmaps, architect marketing data and activation systems, modernize content and loyalty platforms, manage vendor relationships, and drive operational excellence while coaching and growing technical teams to deliver omnichannel personalization and growth.

Lead and mentor a small trade credit analytics team, oversee day-to-day trade credit operations, drive process improvements and controls, produce reports and insights, collaborate cross-functionally, and support team development and project execution to meet KPIs and SLAs.

Lead definition and delivery of AI-powered capabilities for a sustainability real-estate data platform. Shape roadmap, partner with customers and data science, own end-to-end features, define evaluation and trust frameworks, and ensure data foundations support AI. Drive cross-functional alignment and use AI tools in product workflow.

Develop, test, and maintain applications using Java, Python, or Node.js. Write clean, efficient code, debug and optimize, collaborate with cross-functional teams, use data structures/algorithms, work with SQL/NoSQL databases, participate in code reviews, and stay current on AI/ML concepts, agents, and automation tools.

Lead and grow the Client Data Science team (including three direct reports); mentor staff; write and optimize R, Python, and SQL code; apply machine learning and statistical methods; aggregate and analyze multi-source data; create visualizations and reports; liaise with stakeholders; document work in JIRA and present findings to diverse audiences.

Lead design, build, and modernization of recommendation systems integrating backend engineering, data pipelines, and LLMs. Develop, test, deploy, and maintain scalable microservices and MLOps solutions, mentor engineers, collaborate with product teams, and drive system design, monitoring, and retraining pipelines for production ML.

Develop scalable web applications using React and Next.js, implement backend solutions in Python, maintain DevOps practices, and collaborate with AI/ML teams to deploy applications.

The role involves preparing traffic data, developing and deploying machine learning models on Google Vertex AI, and collaborating with teams to improve project workflows.

The Forward Deployed ML Engineer will integrate AI/ML models into clinical software, assist in model fine-tuning, and engage with clients to ensure success in healthcare deployments.

Design, research, and deliver LLM and GenAI solutions for healthcare SaaS at scale: architect systems, handle healthcare data, implement PEFT and RAG pipelines, ensure responsible AI, mentor teams, and translate technical outcomes to business impact.

Design, build, and deploy scalable ML models and pipelines to enhance a talent marketplace and GenAI solutions. Lead experimentation (A/B tests, statistical validation), improve production ML performance, ensure code and data quality, work with cross-functional teams, mentor junior engineers, and propose innovative AI-driven solutions aligned to business goals.

As a Senior Machine Learning Engineer, you will design and maintain ML systems for content recommendations, search, and spam detection, while augmenting user experience and system performance.

The AI/Machine Learning Engineer will develop systems for automating health benefits decisions, integrating large language models with rule-driven patterns, and ensuring compliance with regulations.

Lead design, development, and productionization of advanced ML models and data pipelines. Drive architecture, experimentation, validation, and best practices while mentoring ML engineers and delivering scalable, business-driving AI solutions.

The Machine Learning Engineer will design and build systems that improve the AI shopping agent by using real user feedback, focusing on feedback loops and metrics to enhance user interactions and agent performance.

Design, build, and productionize scalable AI systems for autonomous detection, RAG-backed investigations, and auto-remediation. Develop LLMs, graph-based reasoning engines, streaming pipelines, evaluation/red-team processes, and collaborate with cross-functional teams to translate attacker behavior into robust detections.

Lead design and delivery of scalable AI systems for autonomous threat detection and response. Build and productionize LLMs, graph reasoning engines, streaming feature pipelines, and orchestration frameworks. Own evaluation, reliability, and cross-functional collaboration while mentoring engineers and experimenting with RAG, tool-calling agents, and multi-modal models.

Lead assessment, redesign, and hands-on implementation of data pipelines, ML workflows, and platform infrastructure. Produce prioritized system designs, harden production pipelines, improve observability, reliability, and data quality, and mentor the data team.

Design and operate production ML systems for large-scale streaming data, ensuring reliability and performance while collaborating with researchers to integrate models into distributed systems.

Lead technical roadmap and architecture for ML data infrastructure supporting petabyte-scale pipelines. Manage and mentor engineering team, design ingestion/transformation/serving systems, ensure data quality, lineage, governance, and collaborate with ML and infrastructure teams to deliver production-grade ML data pipelines and feature stores.

Design, develop, and deploy machine learning models; analyze large datasets; build scalable data pipelines and distributed computing environments; integrate ML solutions with cross-functional teams; optimize performance and support production ML infrastructure.

The Machine Learning Engineer will develop software solutions, optimize data pipelines, and deploy machine learning models using cloud platforms and best practices.

The Staff Software Engineer will build and scale machine learning systems for personalization, collaborating with teams to enhance project quality and performance.