Top Machine Learning Jobs in Atlanta, GA

Leads financial statement audits for digital assets and financial services clients, evaluating regulatory compliance, governance, risk management, and internal controls. Applies advanced auditing and technical accounting expertise to identify material misstatements and issue audit reports. Directs complex engagements, coaches diverse teams, manages client relationships, communicates findings, and reinforces professional, independence, and ethical standards. The role also involves data analysis and exploring AI and machine learning platforms to improve processes.

Leads client-facing AI engineering and intelligent automation engagements for health services clients. Manages project planning, budgets, timelines, deliverables, and risks while guiding teams developing machine learning models, algorithms, and cognitive solutions. Reviews Python code and data analysis, validates model performance, coaches team members, identifies automation opportunities, and translates business needs into technical solutions. Promotes responsible AI practices and supports stakeholder collaboration across multiple client workstreams.

Supports data scientists with data exploration, cleaning, analysis, model development, validation, visualization, and prototypes. Uses Python and SQL while learning machine learning, generative AI, cloud data platforms, and production data science practices. Participates in reviews, documentation, presentations, and collaborative projects across multiple co-op terms, progressively increasing responsibility under mentorship.

Leads the design, development, and deployment of scalable AI, generative AI, and machine learning solutions. Oversees data infrastructure, integration, data quality, and advanced analytics while managing client engagements and executive relationships. Drives business development, strategic direction, innovation, and AI-driven automation. Mentors teams, develops future leaders, and fosters collaboration, integrity, and client satisfaction across complex projects.

Leads AI and GenAI data science workstreams for client engagements. Develops machine learning, deep learning, and NLP models; applies statistical and predictive modeling; designs scalable data science workflows and pipelines; evaluates model performance; communicates insights through visualizations and executive narratives; coaches teams; and supports responsible AI governance.

Architect and lead enterprise AI infrastructure supporting LLM training and inference, machine learning, agentic AI, and production platforms. Responsibilities include HPC and GPU architecture, Kubernetes, private and hybrid cloud, high-performance networking, storage, observability, security, reliability, governance, and automation. The role drives technical standards, performance engineering, cross-functional initiatives, emerging technology evaluation, and engineer mentorship in a regulated environment.

Research and develop machine learning and deep learning algorithms for audio applications, including speech enhancement, spatial audio, voice pickup, and hearing augmentation. The intern will build datasets, prototype and integrate models into software or hardware, evaluate audio algorithms, collaborate with interdisciplinary teams, and present findings. Work may contribute to Bose products, patents, and research publications.

Leads technical pre-sales for supply chain integration solutions, partnering with Sales to conduct discovery, design ERP/WMS/TMS and trading-partner architectures, run POCs, deliver demonstrations, and respond to RFPs. Advises technical and executive stakeholders on business outcomes and AI opportunities, translates capabilities into measurable ROI, and collaborates with Product, Customer Success, and Professional Services on roadmap feedback and post-sale handoffs.

Lead product strategy, roadmap, and delivery for Falcon Complete’s internal MDR tooling, including AI-powered investigation and triage, ticketing, case management, and analyst workbench systems. Partner with security operations, analysts, engineering, and data science teams to translate workflows into product requirements, prioritize automation, improve analyst productivity and response times, define success metrics, and enhance customer outcomes.

Designs and advances Finance technology platforms across financial consolidation and Treasury. Leads initiatives, integrates ERP, EPM, and Treasury systems, strengthens data controls and pipelines, and identifies AI-enabled automation opportunities. Partners with Finance stakeholders and engineering teams to translate business requirements into scalable solutions, evaluates emerging technologies, and supports change management, training, and adoption.

Owns product strategy, roadmaps, prioritization, discovery, and delivery for software, data, and AI products supporting behavioral healthcare. Partners with engineering, clinical, community, operations, and executive teams to translate care models into workflows and product features. Leads user research, experimentation, analytics, and field pilots to improve care delivery and outcomes.

Develop and implement data science strategies for machine learning applications including lead scoring, churn modeling, pricing, personalization, and fraud detection. Own end-to-end initiatives spanning data analysis, feature engineering, model training, inference, and monitoring. Collaborate with engineering, infrastructure, analytics, and business teams to deliver measurable impact, improve data capabilities, and communicate findings to technical and non-technical stakeholders.

Leads data ownership and governance for data products, defining metadata, quality, controls, integration, and risk mitigation. Partners with business, analytics, technology, and engineering teams to deliver curated datasets and production-ready solutions. Uses advanced SQL for validation and root-cause analysis, manages workstreams and staff, and supports analytics and machine learning use cases. Applies AI and LLM tooling to automate governance activities and maintains data integrity across regulated environments.

Leads the design and modernization of scalable data architecture for economic data platforms, including massive time-series datasets, archival documents, and research publications. Responsibilities include data modeling, metadata and governance design, storage and processing architecture, indexing, discoverability, security, lifecycle management, and API data access optimization. Partners with application architecture and cross-functional teams to deliver highly available, secure, public-facing data platforms supporting analytics and research.

Develop and deploy AI capabilities across enterprise ERP platforms including Workday and SAP Ariba. Build intelligent agents, chatbots, automation, integrations, and custom GenAI/LLM solutions. Establish PaaS environments, cloud services, deployment pipelines, security, governance, and engineering processes. Lead AI projects from concept through production, evaluate vendor versus custom solutions, support technology roadmaps, and promote AI literacy. Provide production support and training while aligning solutions with business needs and measurable ROI.

Leads client-facing generative AI and data modernization engagements, directing solution design, delivery planning, architecture, and technical decisions. Guides teams in prompt engineering, context engineering, agent orchestration, LLM evaluation, RAG, vector databases, and AI guardrails. Oversees cloud AI implementations using Gemini, Vertex AI, AWS Bedrock, or Azure AI Services; develops Python prototypes; evaluates model performance; manages workstreams and deliverables; and mentors staff on AI methods, agile delivery, and technical problem-solving.

Leads engineering teams delivering scalable AI and machine learning solutions on Google Cloud. Oversees generative AI architecture, Vertex AI, LangChain, LangGraph, LLM APIs, RAG, experimentation, evaluation, guardrails, and LLMOps. Guides technical strategy, client alignment, delivery execution, and team development while applying Python, data science, and machine learning methods to build reliable AI products.

Leads Google Cloud AI and machine learning engineering teams and client workstreams. Oversees generative AI, LLM application, agent orchestration, retrieval-augmented generation, model evaluation, prompt engineering, guardrails, and LLMOps initiatives. Reviews architecture, code quality, testing, and solution delivery using Vertex AI, LangChain, LangGraph, Python, and LLM APIs. Coaches team members, improves reusable delivery assets, and manages client relationships, timelines, quality, and expectations.

Leads client-focused generative AI and data modernization architecture, translating business needs into scalable solutions using Google Cloud, Vertex AI, LLMs, vector databases, and modern data platforms. Advises stakeholders, develops AI evaluation and guardrail practices, guides experimentation and deployment, presents technical recommendations, supports proposals, and coaches teams through complex transformation programs.

Leads People Analytics enablement and BI strategy, overseeing semantic models, certified data products, metric governance, data foundations, advanced analytics enablement, demand prioritization, and adoption. Partners with People Analytics, IT, and HR Strategy teams to create trusted workforce data, reusable semantic assets, and decision-ready insights. Manages teams and operating models while driving data quality, governance, certification, self-service analytics, AI access, and scalable insight delivery across the enterprise.

Build scalable systems and machine learning models that transform satellite imagery into labeled geospatial data and production computer vision products. Responsibilities include developing data and annotation pipelines, tracking lineage and versioning, deploying inference services, evaluating models, supporting cloud infrastructure, and improving labeling workflows. The role requires strong Python, cloud, containerization, CI/CD, infrastructure-as-code, production support, and deep learning expertise, with preferred experience in GCP, Vertex AI, spatial databases, and satellite imagery.

Own and scale Jellyfish’s security engineering program, including strategy, architecture, secure SDLC governance, AI security, threat modeling, vulnerability remediation, incident response, penetration testing, and bug bounty programs. The role combines hands-on software development with executive advising, cross-functional leadership, mentorship, and security risk management across SaaS infrastructure and customer-facing AI products.

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

Own the strategy, roadmap, requirements, launch, adoption, and success metrics for a technical platform. Partner with engineering, data, product, and business teams; evaluate architecture, APIs, and system tradeoffs; prioritize foundational investments against customer and revenue outcomes; and apply AI to improve platform capabilities. The role requires strong product management experience, technical depth, analytical skills, and production experience with LLM-based or ML-adjacent products.

Lead architecture for global cloud-based eCommerce payment platforms. Advise stakeholders on design and technology selection, define multi-year roadmaps, apply AI-enabled architecture patterns, ensure security/compliance, evaluate vendors, and present strategies to senior leaders to modernize merchant acquiring, payment gateway, and developer-facing APIs.