Top Machine Learning Jobs in Atlanta, GA

Define and support AI products for public safety and justice workflows. Conduct user discovery with first responders, dispatchers, and legal professionals; create product requirements and technical specifications; analyze competitors and APIs; monitor generative AI, computer vision, and drone-response trends; and prioritize roadmaps based on impact, feasibility, KPIs, privacy, ethics, and justice-sector regulations.

Lead end-to-end yield optimization for Azure infrastructure by analyzing revenue, hardware, and capacity data. Develop statistical and machine-learning models for pricing, resource allocation, and demand elasticity; design experiments and causal inference analyses; build metrics and dashboards; and communicate actionable recommendations to technical, business, finance, and executive stakeholders. Mentor peers and guide cross-functional decisions involving cloud economics, pricing, customer behavior, utilization, and cost efficiency.

Own customer-facing AI and data engagements from discovery through production launch. Define business value, pilot MVPs, success criteria, and product requirements; prioritize engineering backlogs; lead technical discovery and architecture discussions; manage agile delivery, testing, security, performance, adoption, and stakeholder alignment. Partner with executives, sales, and engineers to deliver production-ready solutions and identify follow-on opportunities.

Lead causal measurement and analytics for media investments: design quasi-experimental and observational approaches, apply interpretable ML and statistics, write reproducible Python/SQL analysis, surface actionable insights for marketing partners, and uphold data ethics and stewardship.

Lead design and implementation of scalable integrations and metadata schema for DAM and MarTech ecosystems. Partner with product, engineering, and marketing teams to enable automated content workflows, improve discoverability, ensure data quality and governance, and integrate content metrics with analytics. Drive taxonomy and schema adoption globally and evolve solutions using AI/ML for classification and tagging.

Advise product, AI, employment, and operations teams on privacy, data protection, ethical AI, and global regulatory compliance. Lead privacy impact assessments, data subject access requests, data processing agreements, cross-border transfer mechanisms, and third-party reviews. Support incident response and remediation, develop scalable privacy and AI processes, and apply AI tools to improve legal operations. The role requires a U.S. law license, privacy law experience, global regulatory knowledge, strong communication, and practical cross-functional collaboration.

Advise state agencies on AI strategy and roadmaps, prioritize use cases, assess readiness and data maturity, define responsible AI governance, facilitate stakeholder workshops, and support early-stage implementation and adoption planning.

Leads logistics business insights by applying advanced analytics, AI, machine learning, automation, and reporting to improve service, cost, capacity, productivity, and sustainability. Builds data foundations, governance, stewardship, and enterprise reporting solutions; deploys digital tools; identifies automation opportunities; tracks KPI value; and coaches logistics teams in data-driven decision-making. Partners with operations, IT, and vendors to embed scalable analytics and digital transformation into logistics workflows.

Leads sustainability and safety data governance, quality, traceability, reporting readiness, and digital transformation across global operating units and bottling partners. Translates business needs into IT requirements, supports Enablon and related platform implementations, improves master data management, and drives analytics, visualization, testing, controls, and process improvements for voluntary and regulatory disclosures.

Develops statistical, machine learning, predictive, and advanced analytical solutions using structured and unstructured public health data. Integrates and analyzes data from surveillance, research, and administrative sources; evaluates data quality; creates visualizations and reports; monitors analytical tools; and translates findings into actionable public health insights. Collaborates with multidisciplinary CDC program teams and communicates technical results to technical and non-technical audiences.

Own and improve Headway’s privacy program across HIPAA, 42 CFR Part 2, state privacy laws, individual rights, incidents, vendor governance, and AI use. Maintain controls, data maps, metrics, training, and operational workflows. Conduct product, vendor, and AI privacy assessments; manage rights requests and incident investigations; coordinate notifications and regulatory submissions; and translate complex requirements into practical guidance for cross-functional teams.

Own the strategy and roadmap for patient-provider search, retrieval, ranking, and marketplace matching. Lead experimentation, objective functions, evaluation, and ML platform foundations while partnering with ML engineers, data scientists, design, clinical teams, and executives. Improve successful, lasting care matches rather than optimizing only clicks or bookings. Balance patient access, provider growth, clinical quality, and payer priorities, while setting the standard for technically grounded product management.

Develop autonomy evaluation metrics, statistical and machine-learning analyses, dashboards, and pipelines for simulation and on-road autonomous vehicle testing. Analyze perception, prediction, and planning performance; identify anomalous behavior and critical scenarios; apply VLMs and LLMs where appropriate; and support release gating and safety decisions. The role requires production Python, C++ debugging, technical leadership, cross-functional collaboration, and rigorous software engineering practices.

Define architecture and metrics for autonomous driving evaluation, lead cross-functional integration across autonomy and simulation teams, develop statistical and machine learning methods, analyze large-scale time-series data, manage evaluation KPIs and release gates, and communicate safety and readiness insights through dashboards and stakeholder presentations.

Serves as the technical bridge between customers, Sales, and technology teams. Leads technical discovery, solution architecture, demonstrations, workshops, proofs of concept, and evaluations using HERE data, APIs, mapping, routing, spatial analytics, cloud, AI, and enterprise integration. Partners with Sales to qualify opportunities, reduce technical risk, develop proposals and reusable presales assets, and guide customers toward location-intelligence solutions.

Analyze malware, binary files, customer detection tickets, and machine learning detections to improve detection efficacy and reduce false positives. Review product detections, assess binary legitimacy, address internal detection questions, and provide insights to data science teams. The role collaborates with technical account managers, incident response teams, data scientists, and malware researchers. Required capabilities include malware analysis, reverse engineering, Python scripting, binary fundamentals, threat assessment, research methods, operating systems, and AI-assisted workflow improvement.

Optimize clients’ logistics, warehousing, transportation, distribution, and merchandising operations using operations research, advanced analytics, and AI. Develop optimization models, translate results into recommendations on cost, service, capacity, and productivity, and support technology implementations from requirements through deployment and adoption. Communicate findings to technical and business stakeholders while delivering client-focused consulting engagements.

Provides consulting support to improve clients’ logistics, transportation, warehousing, distribution, and merchandising through optimization, operations research, advanced analytics, and AI. Develops mathematical models, translates analytical outputs into recommendations, and supports technology and analytics implementations from requirements through deployment. Requires client communication, quantitative problem-solving, and exposure to warehouse operations, labor planning, network optimization, transportation, or distribution.

Designs, engineers, tunes, and maintains security detections, correlation rules, behavioral analytics, and CI/CD pipelines for detection content. Supports network security, access management, incident response, risk assessments, security architecture, and complex troubleshooting. Leads incident recovery, root cause analysis, testing, technical escalations, vendor coordination, and collaboration with security operations, fraud, threat intelligence, infrastructure, and engineering teams.

Leads procurement governance, operating standards, project portfolio oversight, AI adoption, digital transformation, continuous improvement, executive reporting, financial analysis, compliance, and supplier performance initiatives. Establishes frameworks, KPIs, dashboards, workflows, and knowledge systems while coordinating procurement with facilities management and other operational stakeholders.

Analyzes large-scale financial transaction data to identify patterns, anomalies, revenue opportunities, and risk-management insights. Supports THOR data product development, feature engineering, quality assurance, testing, documentation, production validation, and user support. Uses SQL, SAS, Teradata, statistical methods, and machine learning techniques while collaborating with technical and business stakeholders. The role also contributes to data visualization, presentations, training materials, and analytical solutions supporting regulatory compliance and operational efficiency.

Conduct quantitative market and credit risk analytics, stress testing, statistical analysis, and model development. Develop wholesale credit risk models, interpret financial data, prepare technical white papers and documentation, support model validation and monitoring, address model risk findings, and collaborate with technology teams, stakeholders, and senior management. The role also contributes to risk management processes, reporting, automation, and continuous improvement.

Supports AI and model development across Bank of America, applying data science, machine learning, statistics, and analytics to business and risk challenges. Responsibilities include developing scalable solutions, streamlining processes, documenting requirements, assessing AI governance, monitoring models, identifying risks, and communicating recommendations to technical and business stakeholders. The role also promotes AI technology reuse, compliance, automation, and measurable business value throughout the product lifecycle.

Supports client-facing data, analytics, and AI projects by developing data models, dashboards, analyses, and automation solutions. The intern works with structured and unstructured data, researches emerging technologies, identifies process improvements, collaborates with clients, and contributes to presentations and recommendations. The role includes mentorship, professional development, and potential transition to full-time employment.

Leads AI engineering for Coca-Cola’s Fuelight 360 investment optimization platform. Designs and deploys production AI, generative AI, LLM, agent-based, forecasting, simulation, and optimization solutions. Establishes MLOps, LLMOps, AgentOps, governance, observability, security, and engineering standards. Partners with product, data science, engineering, and technology teams to deliver business value while managing, coaching, and developing AI engineering teams.