Top Machine Learning Jobs in Atlanta, GA

Develop and maintain production machine learning models, build scalable training and serving technologies, optimize real-time and batch ML pipelines, and monitor model performance and drift. The role involves deep neural networks, recommendation systems, automated retraining, ML research, engineering excellence, and communicating technical concepts to diverse audiences. Candidates need 6+ years of industry ML experience, strong coding skills, and experience deploying neural networks at scale.

Design, build, deploy, and support scalable Java microservices on AWS. Provide technical leadership, architectural direction, code reviews, troubleshooting, proof-of-concept development, and cross-functional collaboration. Work with high-throughput data, messaging systems, distributed architectures, observability, and AI-assisted development tools. Participate in on-call support, mentor engineers, lead architectural initiatives, and deliver reliable SaaS platform features.

Lead technical client onboarding, migrations, solution design, integrations, data orchestration, troubleshooting, and project delivery for Movable Ink’s AI-powered personalization platform. Partner with clients and internal teams to implement custom workflows, APIs, webhooks, and data solutions. Conduct training and workshops, provide product expertise, advise on roadmap improvements, and support long-term client success across email and mobile marketing programs.

Product Counsel will advise product, engineering, data science, marketing, compliance, and sales teams throughout the product lifecycle. Responsibilities include counseling on regulatory compliance, privacy, intellectual property, consumer protection, AI and machine learning, data rights, platform architecture, APIs, identity and account security, and marketing. The role translates legal requirements into technical guidance, supports AI governance, monitors evolving laws, and develops scalable legal processes for a fast-paced technology company.

Lead GitLab’s company-wide strategy for autonomous, agentic software development workflows. Identify and validate AI opportunities across the SDLC, build prototypes and reference architectures, establish evaluation frameworks and safety controls, and drive internal adoption. Partner across Engineering, Product, Infrastructure, Security, Architecture, and Data and ML teams to scale reliable, observable, compliant agentic capabilities into customer-facing products. Mentor senior technical leaders and represent GitLab externally on AI-assisted development and productivity measurement.

Leads high-visibility product programs focused on market adoption, analyst relations, customer advisory boards, executive briefings, and industry events. Translates technical product roadmaps into compelling market value propositions, synthesizes analyst and customer feedback, and provides actionable insights to product leadership. Partners cross-functionally with Product, Marketing, and executive stakeholders while supporting strategic initiatives and external-facing communications.

Leads technical customer engagements for strategic enterprise accounts, combining solution engineering, software implementation, consulting, and sales support. Responsibilities include discovering requirements, designing and documenting SailPoint solutions, conducting foundational implementations and proof-of-value engagements, educating customers on architecture and best practices, and developing AI-enabled demonstrations and agentic solutions. The role collaborates across sales, product, professional services, partners, and customer success, and may require up to 15% travel.

Build and operate production machine learning systems for Safety AI, including low-latency APIs, data pipelines, model serving, evaluation, monitoring, and rollout infrastructure. Process large-scale camera and telematics data, optimize cloud and edge-to-cloud execution, track model performance and drift, and partner with applied scientists, firmware engineers, platform teams, and product managers to deliver reliable safety features.

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.

Engineer AI-powered threat detections, automated response playbooks, and continuous adversary emulation. Apply machine learning and LLMs to security operations, conduct threat hunting and AI red teaming, map coverage to MITRE ATT&CK, and develop detection-as-code through CI/CD. Build production-grade Python integrations, measurable containment capabilities, and safe autonomous defenses with guardrails. Mentor security professionals and collaborate across threat response, cyber defense engineering, and platform teams.

Design, build, and operate high-throughput, low-latency ad-serving infrastructure processing millions of requests per second. Optimize GPU-powered TensorRT inference, feature stores, model serving, benchmarking, experimentation tooling, and system reliability. Partner with machine learning engineers to productionize models and own systems through design, deployment, observability, capacity planning, incident response, and optimization.

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.

Lead design and delivery of end-to-end revenue optimization products using forecasting, ML/reinforcement learning, and optimization. Partner with cross-functional teams to identify opportunities, prototype solutions, implement analytics, and support deployment while developing the analytics team and driving commercial impact.

Leads cross-functional supply chain optimization across transportation, replenishment, finance, category management, suppliers, and customer delivery. Develops optimization and financial models, analyzes costs and service metrics, identifies process improvements, builds business cases, tracks ROI, and establishes scalable processes. Builds and manages a supply chain optimization team, mentors technical professionals, and supports data-driven decisions involving forecasting, inventory, working capital, and supplier management.

Develops data-driven solutions by integrating and analyzing complex datasets, applying statistical and predictive modeling techniques, and building automation workflows. Uses SQL, Python, Databricks, and Power Automate to create scalable analytics and AI-enabled solutions. Partners with stakeholders across business functions, communicates findings, delivers actionable recommendations, monitors outcomes, and supports data quality, governance, and continuous improvement.

Senior BI Product Manager serving as the subject-matter expert for Quote-to-Cash and Order-to-Cash processes. The role connects financial and operational data to BI, AI, and machine learning opportunities; creates PRDs, roadmaps, and business requirements; and partners with finance, legal, product, delivery, and engineering teams. Responsibilities include improving quoting, contracting, billing, revenue recognition, reporting, and downstream data products across ERP, CPQ, and CLM systems.

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 data management and technical delivery for life sciences consulting engagements. Develops predictive models and advanced analytics using large healthcare datasets, including claims, EHR, sales, payer, and formulary data. Builds internal tools, automates workflows, documents processes, presents data-driven recommendations, and coaches data engineers and junior staff. Supports pharma and biotech clients across commercialization, market access, pricing, reimbursement, regulatory, and compliance initiatives.

Drive recurring revenue growth across Fortune 1000 fleet accounts by managing the full enterprise sales cycle, from prospecting and pipeline development through negotiation and close. Build C-suite relationships, execute consultative direct and channel sales strategies, develop partnerships, support RFX processes, and lead strategic upselling. Maintain accurate forecasting in Salesloft and Salesforce, develop telematics and fleet-management expertise, and represent Geotab at trade shows and industry events.

Leads the architecture and implementation of Azure-based data platforms and machine-learning-driven customer and commercial segmentation. Builds scalable data models, pipelines, and services; operationalizes ML models; establishes monitoring, retraining, governance, privacy, and data-quality standards; and partners with product, business, security, and architecture teams. Acts as a senior individual contributor who guides technical direction, mentors engineers, and delivers cross-team initiatives without direct people management.

Leads the global Voice of Customer strategy, enterprise survey governance, customer-listening programs, and integrated analytics capabilities. Transforms customer, operational, behavioral, and market data into executive insights, dashboards, and actionable recommendations. Owns Qualtrics strategy and vendor performance, advances AI-enabled analytics, and builds a high-performing global customer insights team while driving cross-functional adoption, accountability, and customer-centric business improvements.

Leads complex AI, software, and engineering programs from initiation through delivery. Defines scope, milestones, budgets, resources, and dependencies; manages risks, vendors, and global Agile teams; coordinates engineering, AI, product, UX, and business stakeholders; and provides dashboards, forecasts, financial updates, and executive communication. The role drives alignment and execution across ambiguous, fast-moving technical initiatives.

Build and scale production AI and machine learning systems, including LLM applications, semantic search, RAG pipelines, APIs, data pipelines, and monitoring. Translate business needs into scalable solutions, evaluate and deploy models, conduct design and code reviews, provide technical leadership, mentor junior engineers, support on-call operations, and influence engineering standards and technical direction across healthcare-focused teams.

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.

Performs advanced risk analytics, reporting, data management, visualization, and modeling to identify portfolio trends, risks, control gaps, and business opportunities. Develops SQL-based datasets, dashboards, and executive reporting; investigates data quality and control issues; supports regulatory requirements; and partners with senior stakeholders on strategic initiatives. Uses automation and approved AI tools to improve analytical efficiency, process effectiveness, and risk-management outcomes.