Machine Learning Jobs in Georgia
Machine Learning jobs in Georgia are in strong demand, concentrated in financial technology, healthcare analytics, cybersecurity, and supply chain optimization, with openings at every level from entry-level data associate through senior research scientist. Atlanta is the dominant hiring hub, followed by activity in Alpharetta and Savannah, where employers like NCR Voyix, Equifax, and Delta Air Lines maintain established machine learning and AI teams. Computer vision, natural language processing, and MLOps engineering are among the most sought-after specialties across Georgia's tech corridor. Find a role that fits below and apply directly.
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The Senior Manager, Software Engineer, Data Platform & Segmentation is a senior individual contributor accountable for the technical vision, design, and evolution of data platforms and segmentation capabilities that power Customer and Commercial product teams operating under a modern Product Operating Model.
This role functions as a hands-on technical leader and multiplier, shaping how customer, commercial, and behavioral data is modeled, segmented, and activated across products, enabling better decisions, personalization, and measurable business outcomes.
The role emphasizes deep technical expertise, product partnership, and architectural leadership, rather than people management.
Core Accountabilities
Product Model & Discovery Partnership
- Partner closely with Product Managers, Designers, and Tech Leads to co-own outcomes, not just data assets.
- Participate actively in product discovery to ensure segmentation strategies are technically feasible, scalable, and analytically sound.
- Translate business and customer questions into durable data models and segmentation frameworks.
Machine Learning
- Strong experience in Machine Learning engineering, leveraging ML models to build Segmentation strategies
- Experience operationalizing ML-driven segmentation, including integrating segmentation outputs into other products
- Collaborating with data scientists
- managing standards for model life cycle, monitoring drift, retraining, etc.
- Understanding of Azure ML (or any other cloud) workspaces and integrating them with Azure pipelines
Data Platform & Segmentation Architecture
- Define and evolve the segmentation architecture across customer and commercial data domains.
- Design scalable data models that support real-time, near real time, and batch segmentation use cases.
- Ensure segmentation logic is reusable, explainable, and consistent across channels and products.
- Make explicit trade-offs across latency, accuracy, cost, privacy, and maintainability.
Engineering Execution & Data Quality
- Build and maintain high-quality, production-grade data pipelines and services.
- Ensure strong standards for data quality, lineage, observability, and reliability.
- Reduce fragmentation and duplication in segmentation logic across teams.
- Leverage metrics to continuously improve data freshness, accuracy, and usability.
Individual Contributor Technical Leadership
- Act as a go-to expert for data platform and segmentation design.
- Lead complex technical initiatives end-to-end through hands-on contribution.
- Influence technical direction through design reviews, reference implementations, and documented standards.
- Mentor senior engineers and Tech Leads through coaching and technical guidance (without direct management responsibility).
Microsoft Azure Data Platform & Fabric Expertise
- Demonstrate deep, hands-on expertise with Microsoft Azure data services and their application in large-scale, product-centric environments.
- Design and evolve segmentation and data platform architectures leveraging Azure Data Fabric concepts, ensuring interoperability, governance, and reuse across domains.
- Apply strong architectural judgment across core Azure data products, including data ingestion, storage, processing, analytics, and activation layers.
- Optimize designs across cost, performance, latency, and scalability, using Azure-native capabilities and patterns.
- Ensure secure-by-design implementations aligned with Azure identity, access, encryption, and compliance controls.
- Partner with enterprise architecture, cloud, and security teams to ensure Azure data platform decisions align with broader enterprise strategy while preserving team autonomy.
- Stay current on Azure data platform evolution and proactively assess new capabilities for business value, not novelty.
Business Partnership & Communication
- Serve as a trusted technical partner to Customer and Commercial stakeholders.
- Communicate segmentation concepts, assumptions, and limitations in clear business language.
- Proactively surface data constraints, privacy considerations, and trade-offs to enable informed decisions.
- Support external partner and vendor conversations as a technical authority when needed.
Governance, Privacy & Compliance
- Ensure segmentation approaches comply with data privacy, consent, and regulatory requirements.
- Collaborate with Security, Privacy, and Legal teams to embed governance into platform design—not bolt it on later.
- Advocate for responsible and ethical use of customer and commercial data.
Success Measures
- Segmentation capabilities measurably improve customer engagement and commercial outcomes.
- Reduced duplication and inconsistency in segmentation logic across products.
- Improved data quality, freshness, and trustworthiness.
- Faster time-to-insight and activation for product teams.
- Platforms and models that scale with growth while controlling cost and risk.
Leadership Profile
- Outcome-driven, not data for data’s sake
- Deep technical expertise with strong product intuition
- Influences through credibility and clarity, not authority
- Comfortable operating in ambiguity and evolving problem spaces
- Holds a high bar for data quality, ethics, and reliability
Required Experience & Capabilities
- Bachelor’s degree in Computer Science, Engineering, Data Science, or equivalent experience.
- 8+ years of hands-on experience in data platform, analytics engineering, or backend engineering roles.
- Deep expertise in data modeling, segmentation strategies, and large-scale data systems.
- Strong experience with cloud-native data platforms and modern data tooling.
- Proven ability to partner closely with product and business stakeholders.
- Demonstrated impact as a senior individual contributor on complex, cross-team initiatives.
Pay Range:
United States: 152,000 - 178,300 USDBase pay offered may vary depending on geography, job-related knowledge, skills, and experience. A full range of medical, financial, and/or other benefits, dependent on the position, is offered.
Annual Incentive Reference Value Percentage:
15Annual Incentive reference value is a market-based competitive value for your role. It falls in the middle of the range for your role, indicating performance at target.
Location(s):
United States of AmericaCity/Cities:
AtlantaTravel Required:
00% - 25%Relocation Provided:
NoJob Posting End Date:
September 22, 2026Our Purpose and Growth Culture:
We are taking deliberate action to nurture an inclusive culture that is grounded in our company purpose, to refresh the world and make a difference. We act with a growth mindset, take an expansive approach to what’s possible and believe in continuous learning to improve our business and ourselves. We focus on four key behaviors – curious, empowered, inclusive and agile – and value how we work as much as what we achieve. We believe that our culture is one of the reasons our company continues to thrive after 130+ years. Visit Our Purpose and Vision to learn more about these behaviors and how you can bring them to life in your next role at Coca-Cola.
We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity and/or expression, status as a veteran, and basis of disability or any other federal, state or local protected class. When we collect your personal information as part of a job application or offer of employment, we do so in accordance with industry standards and best practices and in compliance with applicable privacy laws.Pay Range:: 0 - 0 USD
Base pay offered may vary depending on geography, job-related knowledge, skills, and experience. A full range of medical, financial, and/or other benefits, dependent on the position, is offered.
Annual Incentive Reference Value Percentage:15
Annual Incentive reference value is a market-based competitive value for your role. It falls in the middle of the range for your role, indicating performance at target.
Long-term Incentive Reference Value Percentage:0 - 20
Long-term Incentive reference value is a market-based competitive value for your role.
The Senior Manager, Software Engineer, Data Platform & Segmentation is a senior individual contributor accountable for the technical vision, design, and evolution of data platforms and segmentation capabilities that power Customer and Commercial product teams operating under a modern Product Operating Model.
This role functions as a hands-on technical leader and multiplier, shaping how customer, commercial, and behavioral data is modeled, segmented, and activated across products, enabling better decisions, personalization, and measurable business outcomes.
The role emphasizes deep technical expertise, product partnership, and architectural leadership, rather than people management.
Core Accountabilities
Product Model & Discovery Partnership
- Partner closely with Product Managers, Designers, and Tech Leads to co-own outcomes, not just data assets.
- Participate actively in product discovery to ensure segmentation strategies are technically feasible, scalable, and analytically sound.
- Translate business and customer questions into durable data models and segmentation frameworks.
Machine Learning
- Strong experience in Machine Learning engineering, leveraging ML models to build Segmentation strategies
- Experience operationalizing ML-driven segmentation, including integrating segmentation outputs into other products
- Collaborating with data scientists
- managing standards for model life cycle, monitoring drift, retraining, etc.
- Understanding of Azure ML (or any other cloud) workspaces and integrating them with Azure pipelines
Data Platform & Segmentation Architecture
- Define and evolve the segmentation architecture across customer and commercial data domains.
- Design scalable data models that support real-time, near real time, and batch segmentation use cases.
- Ensure segmentation logic is reusable, explainable, and consistent across channels and products.
- Make explicit trade-offs across latency, accuracy, cost, privacy, and maintainability.
Engineering Execution & Data Quality
- Build and maintain high-quality, production-grade data pipelines and services.
- Ensure strong standards for data quality, lineage, observability, and reliability.
- Reduce fragmentation and duplication in segmentation logic across teams.
- Leverage metrics to continuously improve data freshness, accuracy, and usability.
Individual Contributor Technical Leadership
- Act as a go-to expert for data platform and segmentation design.
- Lead complex technical initiatives end-to-end through hands-on contribution.
- Influence technical direction through design reviews, reference implementations, and documented standards.
- Mentor senior engineers and Tech Leads through coaching and technical guidance (without direct management responsibility).
Microsoft Azure Data Platform & Fabric Expertise
- Demonstrate deep, hands-on expertise with Microsoft Azure data services and their application in large-scale, product-centric environments.
- Design and evolve segmentation and data platform architectures leveraging Azure Data Fabric concepts, ensuring interoperability, governance, and reuse across domains.
- Apply strong architectural judgment across core Azure data products, including data ingestion, storage, processing, analytics, and activation layers.
- Optimize designs across cost, performance, latency, and scalability, using Azure-native capabilities and patterns.
- Ensure secure-by-design implementations aligned with Azure identity, access, encryption, and compliance controls.
- Partner with enterprise architecture, cloud, and security teams to ensure Azure data platform decisions align with broader enterprise strategy while preserving team autonomy.
- Stay current on Azure data platform evolution and proactively assess new capabilities for business value, not novelty.
Business Partnership & Communication
- Serve as a trusted technical partner to Customer and Commercial stakeholders.
- Communicate segmentation concepts, assumptions, and limitations in clear business language.
- Proactively surface data constraints, privacy considerations, and trade-offs to enable informed decisions.
- Support external partner and vendor conversations as a technical authority when needed.
Governance, Privacy & Compliance
- Ensure segmentation approaches comply with data privacy, consent, and regulatory requirements.
- Collaborate with Security, Privacy, and Legal teams to embed governance into platform design—not bolt it on later.
- Advocate for responsible and ethical use of customer and commercial data.
Success Measures
- Segmentation capabilities measurably improve customer engagement and commercial outcomes.
- Reduced duplication and inconsistency in segmentation logic across products.
- Improved data quality, freshness, and trustworthiness.
- Faster time-to-insight and activation for product teams.
- Platforms and models that scale with growth while controlling cost and risk.
Leadership Profile
- Outcome-driven, not data for data’s sake
- Deep technical expertise with strong product intuition
- Influences through credibility and clarity, not authority
- Comfortable operating in ambiguity and evolving problem spaces
- Holds a high bar for data quality, ethics, and reliability
Required Experience & Capabilities
- Bachelor’s degree in Computer Science, Engineering, Data Science, or equivalent experience.
- 8+ years of hands-on experience in data platform, analytics engineering, or backend engineering roles.
- Deep expertise in data modeling, segmentation strategies, and large-scale data systems.
- Strong experience with cloud-native data platforms and modern data tooling.
- Proven ability to partner closely with product and business stakeholders.
- Demonstrated impact as a senior individual contributor on complex, cross-team initiatives.
Pay Range:
United States: 152,000 - 178,300 USDBase pay offered may vary depending on geography, job-related knowledge, skills, and experience. A full range of medical, financial, and/or other benefits, dependent on the position, is offered.
Annual Incentive Reference Value Percentage:
15Annual Incentive reference value is a market-based competitive value for your role. It falls in the middle of the range for your role, indicating performance at target.
Location(s):
United States of AmericaCity/Cities:
AtlantaTravel Required:
00% - 25%Relocation Provided:
NoJob Posting End Date:
September 22, 2026Our Purpose and Growth Culture:
We are taking deliberate action to nurture an inclusive culture that is grounded in our company purpose, to refresh the world and make a difference. We act with a growth mindset, take an expansive approach to what’s possible and believe in continuous learning to improve our business and ourselves. We focus on four key behaviors – curious, empowered, inclusive and agile – and value how we work as much as what we achieve. We believe that our culture is one of the reasons our company continues to thrive after 130+ years. Visit Our Purpose and Vision to learn more about these behaviors and how you can bring them to life in your next role at Coca-Cola.
We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity and/or expression, status as a veteran, and basis of disability or any other federal, state or local protected class. When we collect your personal information as part of a job application or offer of employment, we do so in accordance with industry standards and best practices and in compliance with applicable privacy laws.Pay Range:: 0 - 0 USD
Base pay offered may vary depending on geography, job-related knowledge, skills, and experience. A full range of medical, financial, and/or other benefits, dependent on the position, is offered.
Annual Incentive Reference Value Percentage:15
Annual Incentive reference value is a market-based competitive value for your role. It falls in the middle of the range for your role, indicating performance at target.
Long-term Incentive Reference Value Percentage:0 - 20
Long-term Incentive reference value is a market-based competitive value for your role.
See All 20 Machine Learning Jobs in Georgia
Find roles in Georgia that match your experience and apply in just a few clicks.
Find Machine Learning JobsMachine Learning Jobs by City in Georgia
Where Georgia roles are concentrated, by current openings.
Machine Learning Job Market in Georgia
A snapshot from current Georgia openings, updated as new roles post.
Who's Hiring
Top Industries Hiring
- Technology & Software
What Georgia Employers Look For
The qualifications that appear most often in machine learning jobs across Georgia.
- Bachelor's or master's degree in computer science, mathematics, or a related quantitative field
- Proficiency in Python and machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn
- Experience designing, training, and deploying predictive models in production environments
- Familiarity with cloud platforms such as AWS, Google Cloud, or Microsoft Azure for ML workloads
- Strong understanding of data pipelines, feature engineering, and model evaluation techniques
- Ability to communicate model results and business impact clearly to non-technical stakeholders
Machine Learning Jobs in Georgia: Frequently Asked Questions
How do you become a machine learning engineer in Georgia?
Machine learning does not require a state-issued license in Georgia, so the path runs through education and demonstrated skill. Most Georgia employers expect at least a bachelor's degree in computer science, statistics, or a related field, with a master's preferred for research roles. Building a portfolio of end-to-end projects, contributing to open-source work, and earning cloud certifications from AWS or Google Cloud are the credentials that move applications forward in Georgia's competitive market.
Which companies hire machine learning engineers in Georgia?
Employers hiring machine learnings in Georgia right now include The Home Depot, The Coca-Cola Company, and Equifax, based on current listings on Migrate Mate as of September 2026. Georgia's fintech and data-intensive industries mean companies across banking, logistics, and healthcare analytics maintain active machine learning teams year-round.
Which Georgia cities have the most machine learning jobs?
Atlanta and Alpharetta have the most machine learning openings in Georgia. Atlanta drives the majority of demand as home to major fintech firms, Fortune 500 headquarters, and a dense concentration of technology employers, while Alpharetta's growing technology park and Savannah's expanding logistics sector pull additional hiring outside the core metro.
Are there remote machine learning jobs in Georgia?
Yes, and more than most fields. About 82% of machine learning openings tied to Georgia are remote or hybrid as of September 2026, reflecting the desk-based and analytical nature of the work. Model development, research, and data science roles tend to offer the most remote flexibility, while MLOps and production engineering positions are more likely to require on-site or hybrid presence.
How can I get hired as a machine learning engineer in Georgia with little or no experience?
The most realistic entry path is a junior data analyst or associate data scientist role, which large Georgia employers such as Equifax and NCR Voyix use as a pipeline for machine learning talent. Building two or three end-to-end portfolio projects hosted publicly, completing a graduate certificate from Georgia Tech's OMSA program, and targeting internship or rotational programs at Atlanta-area fintech and healthcare companies gives candidates without formal experience a concrete competitive edge.
Where can I find and apply to machine learning jobs in Georgia?
You can find and apply to machine learning jobs in Georgia on Migrate Mate, which lists current Georgia openings from employers actively hiring. Search the roles available, find the ones that fit your background and location, and apply directly.
See All 20 Machine Learning Jobs in Georgia
Find roles in Georgia that match your experience and apply in just a few clicks.
Find Machine Learning Jobs