Top Machine Learning Jobs in Atlanta, GA

Join Trexquant as a Global Alpha Researcher to develop trading signals, implement research, and apply machine learning for alpha discovery.

Lead portfolio-level Data & AI strategy and technical governance for enterprise clients. Advise C-suite, drive architecture and modern data stacks, win high-value engagements, and scale engineering and data science teams.

As a Research Engineer, you'll develop and manage data systems for text-to-speech projects and enhance machine learning models using innovative strategies.

The Engineering Manager will lead teams in developing and managing Blackbaud's Data & AI platform, focusing on scalability, security, and cost-efficiency.

The Staff GenAI Backend Engineer leads engineering for intelligent routing systems, collaborates with teams to enhance customer support, and maintains architectural integrity in a high-availability environment.

Lead a multidisciplinary team to design, deploy, and optimize ML-driven bidding systems and measurement for a nine-figure marketing budget. Drive experimentation, partner strategy, media-mix optimization, and end-to-end measurement while aligning Marketing, Analytics, Engineering, and Product to improve marketing ROI and scale performance.

The AI Solutions Lead will expand Quantiphi's business in key insurance accounts by managing executive relationships, developing account strategies, leading sales initiatives, and ensuring client satisfaction.

Serve as the technical lead in pre-sales for state, local, and education customers: run discovery, design POCs and workshops, deliver demos, map needs to Socure solutions, advise on fraud/risk/compliance, influence product, and mentor junior consultants.

The role involves developing and integrating AI features into Cribl's products, collaborating with teams, and maintaining operational efficiency.

As an Associate Engineer in Data Science, you will leverage data analysis and machine learning to solve complex problems and develop inspiring digital products.

Lead product management for AI tools in software/SaaS, manage product lifecycle, drive strategic roadmap, and ensure product adoption through iterative shipping and user feedback.

As a Principal Product Manager, you'll lead product strategy, collaborate across teams, utilize data for decision-making, and partner with top executives to drive product success.

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

Lead design and scaling of a unified fraud and risk platform. Own roadmap, fraud metrics, SQL-based analysis, A/B testing, real-time detection models, and cross-functional delivery to balance customer experience and risk mitigation.

Lead quantitative credit analytics for a Send Now Pay Later (SNPL) product: build and maintain credit policies, pricing and NPV models, vintage analysis, portfolio monitoring, model integration/testing, and alternative data evaluation to optimize approval rates and manage portfolio health.

Lead fraud detection and mitigation for a conversational BNPL product. Define fraud typologies, own repayment and chargeback analytics, manage fraud KPIs, design operational policies, collaborate on ML models and decisioning workflows, and coordinate SARs and compliance reporting.

Lead fraud detection and mitigation for a conversational Send Now Pay Later product: define first-party fraud typologies, own repayment and chargeback analytics, manage fraud KPIs, design operational policies, integrate ML and decisioning tools (Taktile), and coordinate SARs and compliance reporting.

Lead credit analytics for the Send Now Pay Later product: build and maintain credit policies, pricing models, vintage and NPV analyses, executive reporting, model integration and testing, and explore alternative data to optimize approval rates and portfolio health.

Lead credit analytics for the Send Now Pay Later product: build and optimize valuation, vintage and NPV models; manage credit policies, portfolio monitoring, and NPL reporting; integrate and test scoring models with engineering/data science; and research alternative data to improve predictive performance.

Lead fraud detection and mitigation for a conversational BNPL product: define fraud typologies, analyze repayment/chargeback trends, own fraud KPIs, design policies, collaborate on ML models and decisioning workflows, and coordinate SARs with Compliance.

Lead fraud risk for a conversational lending product (SNPL): define first-party fraud typologies, analyze repayment and chargeback trends, own fraud KPIs, design automated/manual blocking policies, coordinate ML/rules with central Fraud and Data teams, and liaise with Compliance on SARs and loan abuse.

The role oversees analytical characterization and comparability studies for biologics, ensuring regulatory compliance and leadership in product development.

The role involves building predictive models for blockchain performance and user behavior, implementing solutions, and owning production prediction pipelines.

The Engineering Manager for Smart Signals leads a team, manages execution, and maintains technical depth in backend systems for fraud detection. They shape strategy and coordinate across teams to enhance product quality.

Design and build backend systems and APIs for AI-driven products, ensuring scalability, reliability, and integration of ML models while collaborating with engineering and product teams.