Senior ML Engineer Jobs in Georgia
Senior ML Engineer jobs in Georgia are among the most active in the Southeast, concentrated in fintech, healthcare AI, cybersecurity, and logistics optimization, with demand from entry-level ML engineers through principal and staff-level roles. The heaviest hiring is in Atlanta, with secondary activity in Augusta and Savannah, where employers like Delta Air Lines, NCR Voyix, and Equifax maintain substantial data and AI teams. The most sought-after specializations in Georgia right now are large language model deployment, real-time inference systems, and MLOps. Find a role that fits below and apply directly.
Find Senior ML Engineer JobsOverview
Showing 5 of 17+ Senior ML Engineer jobs
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 17 Senior ML Engineer Jobs in Georgia
Find roles in Georgia that match your experience and apply in just a few clicks.
Find Senior ML Engineer JobsSenior ML Engineer Jobs by City in Georgia
Where Georgia roles are concentrated, by current openings.
Senior ML Engineer 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 senior ML engineer jobs across Georgia.
- Bachelor's or master's degree in computer science, statistics, or a closely related field
- Five or more years of applied machine learning experience in production environments
- Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, or JAX
- Demonstrated experience designing and deploying end-to-end ML pipelines at scale
- Familiarity with cloud platforms including AWS, Google Cloud, or Azure ML services
- Strong communication skills for presenting model results to non-technical stakeholders
Senior ML Engineer Jobs in Georgia: Frequently Asked Questions
How do you become a senior ml engineer in Georgia?
Georgia does not require a state-issued license to work as a senior ml engineer, so the path is credential and experience driven. Most roles expect a bachelor's or master's degree in computer science, mathematics, or a related field, combined with several years building and shipping production ML systems. Georgia's deep concentration of technology employers in Atlanta creates a clear pipeline from junior ML or data science roles up to senior positions, and certifications from cloud providers like AWS or Google Cloud carry real weight with local hiring managers.
How much do senior ML engineers make in Georgia?
Senior ML engineers in Georgia earn a median of about $104,610 a year, based on May 2025 Bureau of Labor Statistics wage data, ranging from around $60,920 for the lowest 10% to over $173,060 for the top 10%. Pay rises with experience, specialty, and employer.
Which companies hire senior ml engineers in Georgia?
Employers hiring senior ml engineers in Georgia right now include The Coca-Cola Company, Equifax, and The Home Depot, based on current listings on Migrate Mate as of September 2026. Georgia's fintech corridor, anchored in Atlanta, along with major logistics and healthcare companies headquartered in the state, keeps demand for experienced ML talent consistently high.
Which Georgia cities have the most senior ml engineer jobs?
Atlanta and Alpharetta have the most senior ml engineer openings in Georgia. Atlanta drives the overwhelming majority of postings because it is home to the regional headquarters of major fintech, media, and logistics firms, while Augusta and Savannah contribute openings tied to defense contractors, healthcare systems, and growing technology campuses expanding outside the Atlanta metro.
Are there remote senior ml engineer jobs in Georgia?
Yes, and more than most fields. About 78% of senior ml engineer openings tied to Georgia are remote or hybrid as of September 2026, reflecting how much of the work involves writing code, training models, and reviewing pipelines rather than being physically on-site. Model deployment and infrastructure work tends to be the most remote-friendly portion of the role.
How can I get hired as a senior ml engineer in Georgia with little or no experience?
The most realistic entry path is moving from a data scientist, data analyst, or software engineer role into an ML-focused position. Large Georgia employers like NCR Voyix, Equifax, and Home Depot run new-grad rotational and associate engineering programs that include ML tracks, giving early-career candidates structured exposure to production systems. Building a portfolio of end-to-end projects on public datasets, contributing to open-source ML tools, and earning a cloud ML certification from AWS or Google significantly strengthens a Georgia application with limited professional ML history.
Where can I find and apply to senior ml engineer jobs in Georgia?
You can find and apply to senior ml engineer jobs in Georgia on Migrate Mate, which lists current Georgia openings updated regularly. Search the listings to find roles that match your experience and specialization, then apply directly to the ones that fit.
See All 17 Senior ML Engineer Jobs in Georgia
Find roles in Georgia that match your experience and apply in just a few clicks.
Find Senior ML Engineer Jobs