Top Senior Machine Learning Jobs in Atlanta, GA

Design, deliver, and maintain revenue-optimizing analytics and optimization products. Identify commercial opportunities, build models (forecasting, RL, optimization), process large/unstructured data, coordinate stakeholders and IT, implement end-to-end solutions, and support Analytics team development.

The Senior Product Manager will oversee product development in the LATAM market, focusing on infrastructure, product strategy, and user engagement metrics while coordinating with various stakeholders.

Lead architecture, design, implementation, and global rollout of Varicent ICM solutions. Translate compensation plans into scalable Varicent Designer/Studio configurations, integrate with Salesforce/SAP/Workday, enable AI/ML use cases, enforce governance/SOX controls, manage upgrades, mentor teams, and support stakeholders across Sales, Finance, and IT.

As an Enterprise Account Executive at Salesforce, you will drive revenue growth by leveraging AI-driven solutions, understanding client needs, and demonstrating how Agentforce can solve business challenges while fostering client relationships.

Design and maintain enterprise data architecture and pipelines to integrate legal and business systems. Define data strategy, governance, and analytics infrastructure. Support practice groups with legal technology, AI/ML integration, vendor assessments, documentation, training, and risk/compliance controls across marketing, finance, HR, and legal operations.

Work with sales and partners to solve complex customer problems, deliver value-based demos and proofs of concept, map customer architectures to Snowflake Data Architecture, support design and implementation, and help close large enterprise deals while maintaining expertise in data warehousing, ETL, analytics, and ML/generative AI.

Lead AI strategy and roadmap development for state agencies: assess readiness, prioritize use cases, advise leadership on investments, define responsible AI governance, and support early implementation planning and stakeholder alignment.

Lead middle-office trade lifecycle from capture through settlement across equities, fixed income and complex assets. Manage trade matching, confirmations, corporate actions, reconciliations, controls and stakeholder relationships. Drive process improvement and automation (AI/RPA), ensure regulatory/compliance adherence, deliver reporting, and coach onshore and offshore teams to optimize settlement efficiency and reduce exceptions.

The Client Partner will drive adoption of the Aera Decision Cloud by engaging with senior executives at enterprise clients, identifying opportunities, and managing client relationships. Responsibilities include strategic planning and achieving revenue targets in a consultative sales approach.

The Sr. Manager of Information Risk & Controls drives the risk management evolution by leading risk identification, developing frameworks, and collaborating with teams across Invesco.

As Director, Global Deal Desk, you'll architect the Quote-to-Cash process, integrate AI automation, mentor teams, and optimize deal negotiations for revenue capture.

Lead development of digital twin and simulation models for warehouse operations, deploy AI-driven decision systems, build forecasting and optimization models, validate experiments linking operational metrics to financial impact, and partner with engineers, operations, and external AI/academic partners to scale innovations across fulfillment networks.

The Data Scientist will optimize global shipping using data science to build predictive models, create dashboards, and enhance operational performance. Requires strong skills in AI and collaboration.

Lead the Logistics Insights team to convert data into actionable insights using advanced analytics, AI/ML, and automation. Own the logistics analytics roadmap, partner with leadership to deploy digital capabilities, improve service, cost, and capacity agility, and establish data governance and enterprise-ready reporting to drive measurable operational improvements.

Design, build, and deploy production-grade ML systems for ICS/xOT cybersecurity, including threat detection, anomaly detection, NLP/LLM applications, and scalable data pipelines with observability and MLOps practices for cloud and on-prem environments.

Design, build, and deploy production ML systems for ICS/xOT cybersecurity, including threat detection, anomaly detection, NLP/LLM features, data pipelines, observability, MLOps, and containerized deployments across cloud and on-prem environments.

The Machine Learning Engineer develops production ML algorithms and recommendation systems, collaborates with teams, and optimizes user experiences.

Manage a team of ML engineers to build and optimize Affirm's economic decisioning engine using advanced ML techniques. Collaborate across teams, provide technical leadership, and mentor engineers while driving a cohesive long-term ML strategy.

Build and scale predictive models and ML-driven forecasting for FP&A; develop data pipelines, model governance, monitoring, and self-service analytics; generate actionable financial insights and drive AI adoption across finance while ensuring data integrity and communicating results to leadership.

Lead customer-facing data science engagements: manage data acquisition and cleaning, build and evaluate scalable models, drive model best practices and ethics, write extensible code, research industry trends, define solution strategy, and collaborate across engineering and product teams to deliver AI/cloud solutions.

Lead design, build, and productionize ML models and agent workflows. Own model selection strategy, AI governance (bias, compliance, audit trails), and evaluation systems using the GenAI Eval Framework. Coach teams on AI adoption, build monitoring/infrastructure, benchmark models for cost/quality, curate labeled datasets, detect drift, and translate model results to business value.

The Sr. Machine Learning Engineer will develop and deploy ML solutions for healthcare, manage data pipelines, and work with large datasets to enhance healthcare delivery.

Lead HR Operations and Total Rewards with an AI-native mindset. Own compensation, equity, benefits, HRIS (Workday) operations, ATS (Ashby) administration, workforce analytics, compliance, and immigration. Partner with Enterprise Technology to embed LLMs and automation, build self-service HR experiences, manage vendor renewals, and lead a team delivering people insights to executives.

Senior pre-sales engineer who partners with sales and channel teams to present Snowflake, run demos and proofs-of-concept, design solutions, and close large enterprise deals. Requires deep expertise in databases, cloud data architectures, ML/generative AI, SQL and Python, and the ability to engage both technical and executive stakeholders.

As a Staff Applied ML Scientist, you will develop and maintain fraud detection models, conduct research on fraud types, and collaborate across teams to drive product development and real-time decision-making.