Top Machine Learning Jobs in Atlanta, GA

Leads internal audit and SOX engagements, manages client relationships, develops audit methodologies, evaluates financial statements and internal controls, conducts risk assessments, and oversees audit reporting. The role includes project ownership, budgeting, stakeholder management, team leadership, mentoring, process improvement, and use of AI and risk technology to enhance audit effectiveness and address compliance and governance risks.

Supports cybersecurity, data protection, and privacy consulting projects by researching privacy regulations, analyzing privacy impact assessments, assisting with compliance training and privacy program strategies, and helping identify cyber risks and data protection challenges. The intern collaborates with advisory teams, contributes to data breach prevention efforts, and develops foundational skills in technology consulting, privacy compliance, and cybersecurity risk management.

Provides analytics-driven advisory support for mergers, acquisitions, and other business transactions. Analyzes contract data to identify risks, obligations, performance insights, and compliance indicators; develops algorithms, data models, pipelines, visualizations, and machine learning solutions. Integrates multiple data sources, validates data integrity and security, communicates findings to clients, and mentors junior team members.

Leads the design, development, and maintenance of enterprise data systems supporting manufacturing data, analytics, reporting, and AI. Oversees scalable cloud-based solutions, data pipelines, infrastructure, modeling, formats, and frameworks. Partners with stakeholders to align data capabilities with business needs and manages a high-performing data engineering team through coaching, performance management, and organizational development.

Leads teams designing and deploying AI-driven enterprise and cloud security solutions. Responsibilities include developing machine learning systems, integrating data infrastructure and pipelines, performing advanced modeling and analysis, and building AI applications with Python, Java, and C++. The role involves client engagement, strategic problem-solving, stakeholder validation, coaching, process innovation, and operational excellence while addressing complex cybersecurity and privacy challenges.

Lead the product strategy and architecture for Zeta’s agentic AI platform, including LLM agent orchestration, context graphs, real-time context streaming, telemetry, observability, evaluation suites, model workbench capabilities, and MCP tool governance. Align Product, Engineering, Data Science, Design, Security, and business teams around platform standards and priorities. Prototype solutions, troubleshoot technical issues, and establish feedback loops for continuous improvement of agent performance and user outcomes.

Design, build, operate, and scale enterprise AI platforms across public and private clouds. Develop MLOps, automation, CI/CD, infrastructure, observability, monitoring, governance, and deployment capabilities for machine learning and generative AI workloads. Support production systems, incident response, troubleshooting, reliability, and lifecycle management. Collaborate with engineering, infrastructure, security, and governance teams, evaluate emerging technologies, contribute to architecture and strategy, and mentor peers.

Leads Toast’s AI and analytics strategy for Enterprise Care. Owns the roadmap, evaluates and deploys AI-assisted support tools, develops operational intelligence products, and partners with Product, Engineering, and Data teams to move pilots into production. Establishes complexity and workload intelligence, assesses AI readiness, translates AI performance into measurable operational outcomes, and builds organizational AI literacy. The role requires extensive enterprise support or service-delivery leadership, hands-on production AI experience, operational data fluency, and the ability to drive trusted adoption among agents and executives.

Build and productionize traditional machine learning models, owning feature engineering, validation, deployment, monitoring, drift detection, and retraining. Develop LLM-powered agentic tools, dashboards, self-serve analytics products, and automated insight pipelines. Partner with engineering and business stakeholders, write production-grade Python and SQL, drive adoption, improve data quality, and establish responsible AI workflows for a regulated industry.

Designs and implements scalable AI, machine learning, automation, and generative AI solutions for clients. Responsibilities include assessing AI readiness, developing strategies, leading discovery and solution delivery, integrating AI with enterprise systems, advising on governance and responsible AI, managing client engagements, contributing to reusable AI offerings, and supporting business development. The role requires strong technical, consulting, communication, and stakeholder management skills.

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.

Analyze malware, binary files, customer detection tickets, and machine learning detections to improve detection efficacy and manage false positives. Review product detections, assess legitimacy and risk, 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.

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.

Lead AI engineering teams to design, build, and deploy end-to-end AI solutions. Manage client relationships, plan and deliver projects, mentor staff, drive adoption of cloud-native microservices, CI/CD, vector databases and orchestration tools, and ensure high standards of quality, innovation, and compliance.

Develop, test, and integrate AI software for aerospace and industrial applications. Interns will use Python, JavaScript, and C++ to build AI solutions, evaluate data and algorithms, collaborate with machine learning and data engineers, monitor AI trends and regulations, and present progress to leadership and stakeholders.

Design and lead the technical strategy for search across Vannevar’s products. Build and improve full-text, vector, and LLM-powered retrieval systems, optimizing latency, relevance, index quality, freshness, and performance. Collaborate with ML, product, design, and mission strategy teams to translate user needs into search innovations. Apply expertise in information retrieval, NLP, machine learning, indexing, ranking, query parsing, tokenization, vector search, and evaluation frameworks.

Develop and deploy machine learning models powering real-time bidding, advertising decisions, reporting, and customer predictions. Solve propensity modeling, bidding strategy, A/B testing, CLTV, and churn prediction problems at large scale. Build enterprise-grade data and machine learning platforms, write modular production code, collaborate with engineers, and own projects from concept through implementation.

Supports commercial enterprise sales by discovering customer requirements, aligning SailPoint solutions, delivering demonstrations, responding to RFPs, and leading Proof of Value evaluations. The role partners with sales, product, customer success, professional services, and channel teams while communicating technical and business value to IT and business stakeholders. It requires substantial pre-sales experience, enterprise software knowledge, SaaS architecture familiarity, and expertise in identity governance and access management.

Lead Runpod’s Americas sales organization and revenue number across new business, expansion, and renewals. Manage and coach Account Executives and Account Managers, oversee forecasting and pipeline, lead complex enterprise deals and contract negotiations, build regional go-to-market strategy and sales playbooks, and convert product-led demand into revenue. Partner with Marketing, Product, Legal, and Finance while improving sales efficiency through AI and developing a remote-first team.