Top Machine Learning Jobs in Atlanta, GA

Analyze structured and unstructured data, build and enhance statistical and machine learning models in Python, integrate data sources, identify customer trends and insights, support pricing and elasticity analysis, and present complex analytical findings to business leaders and upper management.

Own the design vision and execution for experimental AI-powered learning tools co-created with teachers and researchers. Design teacher- and student-facing products, create flows, wireframes, interaction designs, and functional prototypes, and facilitate collaboration across educators, researchers, engineers, and data scientists. Conduct usability testing and validate research hypotheses while navigating ambiguity, data-heavy workflows, and uncertain AI/ML-enabled features.

Design, build, test, and improve plugin-based forecasting models from business need through production deployment. Own ML and modeling initiatives end to end, evaluate performance across customer environments, distinguish generalizable solutions from customer-specific requirements, and use AI-assisted engineering tools. Work autonomously with customers and cross-functional stakeholders in a distributed international team, with occasional travel.

Leads enterprise AI enablement and intelligent automation, overseeing use-case intake, prioritization, delivery, governance, adoption, and value realization. Establishes the AI operating model, builds literacy and training programs, manages automation and AIM teams, sets responsible-AI guardrails, tracks adoption metrics, and aligns investment with enterprise transformation priorities. Requires executive stakeholder leadership, portfolio governance, team development, and experience scaling AI or automation capabilities in complex organizations.

Develops strategic relationships and revenue growth with universities and higher-education research institutions. Owns the complete sales cycle, from prospecting and discovery through negotiation and closing, while meeting quotas and forecasting revenue. Creates territory and account plans, translates complex software and computing requirements into NVIDIA solutions, and collaborates with solution architects, product teams, developer relations, business units, and channel partners. Requires consultative technology sales expertise and understanding of AI, accelerated computing, and research environments.

Architect and scale cloud-based ML infrastructure supporting large-scale training, massive datasets, model development, and customer services. Own platform design, implementation, deployment, monitoring, reliability, cost optimization, and usability. Collaborate with data science, product, and operations teams, solve complex distributed-systems challenges, and mentor engineers while driving technical innovation.

Leads enterprise manufacturing AI, machine learning, and Industry 4.0 initiatives across multiple plants. Develops and deploys solutions for predictive maintenance, process and quality optimization, energy efficiency, throughput, and OEE improvement. Establishes scalable data and AI platforms, governance, and MLOps standards while integrating industrial data sources. Leads and mentors data science teams, prioritizes projects, allocates resources, partners with cross-functional manufacturing stakeholders, and presents strategic recommendations and business cases to executives.

Manage paid social campaigns and full-funnel strategies across Meta, LinkedIn, TikTok, Pinterest, Snapchat, and other paid channels. Oversee client accounts, analyze performance, identify growth opportunities, build client relationships, and collaborate with SEO, creative, and analytics teams. Contribute to thought leadership, coach associates, maintain quality standards, and explore automation, AI, and LLM search visibility initiatives.

Own the strategy, execution, customer success, and go-to-market for Oscilar’s Device and Behavior product. Lead development of SDKs, signal pipelines, behavioral biometrics, agentic identity signals, data networks, foundation-model capabilities, and analyst experiences. Partner with sales and customers to drive adoption, shape product positioning, and deliver a device intelligence and digital identity platform for fraud, onboarding, authentication, and risk decisions.

Supervise actuarial predictive modeling initiatives for insurance pricing, portfolio management, and planning. Collaborate with actuaries, data scientists, underwriters, and management to develop, validate, govern, and implement models. Establish assumptions, loss adjustments, usage guardrails, monitoring practices, and model documentation. Support regulatory filings, improve team workflows, contribute to strategic projects, and mentor staff in actuarial and technical skills.

Develop and maintain pricing and predictive models for credit insurance products. Analyze claims, loss ratios, portfolio performance, profitability, credit risk, and economic scenarios. Recommend pricing actions, support forecasting and reserves, evaluate underwriting strategies, and partner with underwriting, finance, claims, data science, and technology teams. Ensure model governance, documentation, and actuarial standards compliance.

Build and operate production-grade AI data pipelines and financial knowledge services for equities content. Responsibilities include processing text, tables, documents, and multimedia; creating knowledge bases and search indexes; engineering RAG retrieval and citation systems; integrating LLMs and NLP models; managing orchestration, APIs, quality, cost, observability, reliability, and scalable production operations.

Staff-level ML and agent systems engineer responsible for production model lifecycles, multi-step agentic systems, evaluations, retrieval and context engineering, and model cost and latency optimization. The role owns technical direction for Code Understanding products, improves model and agent quality, establishes evaluation and monitoring standards, mentors engineers, collaborates with customers, and delivers reliable AI-powered developer experiences at enterprise scale.

Designs, builds, and operationalizes enterprise AI, generative AI, machine learning, and agentic AI solutions. Develops integrations, orchestration services, reusable tooling, deployment pipelines, monitoring, and governance controls. Partners with architecture, platform, data, and product teams to deliver scalable, secure, compliant AI capabilities across the organization.

Designs and governs enterprise AI architecture, including generative AI, agentic workflows, RAG, orchestration, integrations, governance, security, compliance, and AI operations. Establishes scalable reference architectures, platform strategies, responsible AI guardrails, lifecycle management, monitoring, and cost controls. Partners with enterprise architecture, engineering, data, and product teams to drive secure AI adoption across cloud services, APIs, and enterprise platforms.

Owns the strategy, roadmap, requirements, backlog, positioning, and lifecycle execution for proprietary software products. Partners with engineering, UX, QA, architecture, data operations, and project management teams in an Agile environment. Conducts market and competitive analysis, supports product launches, creates AI prototypes, develops client-facing content, and contributes to RFP responses. Requires strong technical, product leadership, communication, prioritization, and stakeholder-management skills.

Lead MLOps efforts to productionize machine learning models: build containerized deployments, automate CI/CD pipelines, manage Azure-based ML lifecycle, monitor model performance and data drift, collaborate with data engineering on pipelines, and mentor junior MLOps engineers while driving engineering best practices.

Designs and implements Dynamics 365 Finance solutions supporting General Ledger, payables, receivables, cash management, fixed assets, consolidations, integrations, reporting, and financial processes. Leads fit-gap analysis, project delivery, optimization, AI and automation initiatives, stakeholder engagement, audit support, training, mentorship, and finance systems activities related to mergers and acquisitions. Advises leadership on D365, Power Platform, Copilot, data, and emerging Microsoft technologies.

Lead large-scale Workday Financials implementations and delivery of embedded AI, machine learning, and agentic finance solutions. Responsibilities include configuring business processes and security, developing integrations, managing data loads and tenant migrations, designing finance ecosystems, facilitating workshops, coordinating technical and functional workstreams, and advising senior stakeholders. The role also mentors consultants and analysts while driving automation, predictive forecasting, and scalable finance transformation initiatives.

Lead and transform the Fraud Intelligence Team by designing scalable operating models, QA and measurement frameworks, and talent programs. Drive labeling quality, operational metrics, and fraud prevention impact; convert frontline fraud observations into insights for Product, R&D, Analytics, and GTM; leverage AI, automation, and tooling; and serve as a cross-functional fraud subject-matter expert while developing early-career talent and measurable business outcomes.

Lead a data science and AI team by overseeing advanced analytics, machine learning, AI systems, automation, data workflows, model governance, and productionization. Responsibilities include coaching staff, allocating resources, ensuring technical rigor and reproducibility, managing delivery outcomes, partnering with engineering and business leaders, and shaping enterprise analytics and AI strategy.

Build rigorous ROI analyses, value models, and business cases that translate AI ranking performance into revenue, margin, and cost-to-serve outcomes. Present analyses to CFOs, procurement teams, and other economic buyers; support sales pursuits; create reusable value-selling frameworks; tailor models to customer economics; and collaborate with product, data science, and customer teams to validate benchmarks and deployment results.

Own the full Global System Integrator partner lifecycle, including sourcing, agreements, joint go-to-market motions, enablement, delivery planning, and performance optimization. Build relationships across major GSIs such as Accenture, Deloitte, Bain, BCG, Slalom, and IBM. Collaborate with sales, product, and engineering teams to generate partner-sourced and influenced pipeline and revenue. Develop the GSI strategy, playbook, and operating processes for Sequen’s growing AI business.

Drive revenue retention and account growth across a portfolio of advertising clients. Responsibilities include developing account growth plans, managing forecasts and pipelines, identifying upsell and cross-sell opportunities, fostering product adoption, delivering data-driven campaign insights, leading QBRs, and coordinating Analyst, AdOps, Product, and Creative teams to resolve issues and achieve client outcomes.

Own the strategy, roadmap, packaging, pricing, design partnerships, and go-to-market launch of brightfin’s AI-native FinOps and ITFM product pillar. This individual contributor role will define the MVP, validate market demand, secure initial revenue, and scale the offering toward meaningful ARR. The leader must have extensive AI product leadership experience, direct ITFM/TEM/FinOps expertise, enterprise governance knowledge, and proven ownership of pricing, positioning, sales enablement, and commercialization.