Interested in this AI/ML Engineer role at The Coca-Cola Company?
Apply Now →About This Role
The media industry is in the midst of a structural shift unlike any in decades, driven by automation, algorithmic optimization, and generative AI. These forces are adding complexity to traditional planning, buying, and creative processes — converging first\-party data, synthetic content, and commerce into a single AI\-mediated ecosystem.
For The Coca‑Cola Company, this transformation presents enormous opportunities and critical governance challenges. The Senior Director, Global Media AI Governance \& Investment Integrity will lead the strategy and execution of global governance frameworks for AI\-driven media, ensuring transparency, brand safety, and ethical practices safeguard business growth in an increasingly automated landscape.
Based in Atlanta or London with global accountability, this role combines governance leadership with deep technical fluency. As a people leader and network builder, you will partner with agencies, platforms, regulators, and internal teams to position The Coca‑Cola Company at the forefront of responsible innovation, creating scalable systems that interrogate algorithms without stifling progress.
Key Responsibilities
AI\-Era Media Governance Architecture
- Lead design of The Coca‑Cola Company’s AI Media Governance Framework, including policies, standards, and operational controls for algorithmic planning, buying, and generative creative.
- Build a classification system for AI decisions—from human\-led through fully autonomous—and define oversight protocols for each category.
- Formalize disclosure and accountability standards to ensure transparency across agencies, platforms, and third parties.
Investment Quality \& AI Performance Integrity
- Define global governance benchmarks for AI\-era investments, moving beyond legacy metrics to attention, authenticity, and contextual alignment.
- Deploy an AI Investment Quality Dashboard, surfacing verification and brand safety insights for executive decision\-making.
- Establish The Coca‑Cola Company’s policy on relevance vs. reach trade\-offs, balancing algorithmic outputs with sustainable brand equity.
Algorithmic Transparency \& Brand Safety
- Set brand safety governance for AI\-mediated environments, ensuring calibration of contextual classifiers and prevention of harmful adjacency.
- Implement AI\-driven SPO governance and MFA mitigation powered by algorithmic evaluation of supply quality.
- Publish quarterly AI Media Transparency Reports to leadership outlining risk, compliance health, and AI usage footprint.
Agency \& Platform AI Accountability
- Redesign agency scorecards to add AI governance KPIs such as transparency audits and bias reporting.
- Negotiate AI\-specific terms into contracts with agencies and platforms (Google, Meta, Amazon, TikTok, Trade Desk).
- Institutionalize structured AI governance reviews in global QBRs and annual evaluations to ensure partner accountability.
AI \& AdTech Governance — Data, Privacy \& Identity
- Direct governance over AI\-augmented media stacks inclusive of DSPs, AI\-driven creative platforms, and ID resolution systems.
- Define usage standards for first\-party data in AI modeling compliant with GDPR, CCPA, and evolving AI legislation (such as the EU AI Act).
- Anticipate and respond to regulatory shifts, embedding compliance into operational media workflows globally.
AI\-Augmented Governance Operations \& Capability
- Build and deploy AI\-powered governance operations using LLM\-based contracting, anomaly detection, and automated compliance pipelines.
- Deliver AI literacy programs globally for marketing teams to ensure safe, informed adoption of algorithm\-driven execution.
- Maintain a live AI Governance Playbook refreshed quarterly reflecting regulatory and technology evolution.
- Represent The Coca‑Cola Company within industry forums (WFA, IAB Tech Lab, TAG) to shape and adopt cross\-industry AI advertising governance principles.
Qualifications \& Experience Required
- Bachelor’s degree in Marketing, Business, Data Analytics, or a related discipline; advanced degree preferred.
- 12\+ years in media governance, investment integrity, or adtech/martech operations with global scope, ideally at the leading edge of AI and digital media.
- Balances bold experimentation with sound governance, driven by relentless curiosity to lead through an ever\-evolving landscape.
- Expertise in AI\-optimized media systems and platforms (Google Performance Max, Meta Advantage\+, DV360 AI, The Trade Desk Koa).
- Ability to embed governance standards in contracts, performance metrics, and operational monitoring globally.
- Strong understanding of programmatic ecosystems (DSPs, clean rooms, ID frameworks) and related algorithmic controls.
- Familiarity with evolving AI regulatory requirements and applications in media governance (GDPR, CCPA, EU AI Act).
- Leadership in team development, risk management, and influencing multi\-stakeholder environments.
- High digital and AI fluency supported by frequent use of compliance automation and AI collaboration tools.
What We’ll Do for You
- Position you as a strategic leader shaping global AI governance practices, ensuring Coca‑Cola operates responsibly and competitively in an evolving media landscape.
- Provide access to an enterprise\-level innovation agenda leveraging AI\-powered compliance tools, dynamic dashboards, and advanced adtech solutions.
- Empower your leadership impact globally through deep engagement with markets, agency holding companies, key platforms, and global governance forums.
- Offer a platform for professional growth at the intersection of media, technology, ethics, and business strategy through ongoing learning and real\-world transformation initiatives.
This role may be located in Atlanta, Georgia or London, United Kingdom. Candidates must be legally authorized to work in the country in which they are applying. Candidates applying for UK\-based positions must already reside in the UK and possess the required work authorization. Relocation assistance is available only for eligible domestic relocations within the United States and is not available for international relocation to the United States or the United Kingdom.
The Coca\-Cola Company will not offer sponsorship for employment status (including, but not limited to, H1\-B visa status and other employment\-based nonimmigrant visas) for this position. Accordingly, all applicants must be currently authorized to work in the United States on a full\-time basis and must not require The Coca\-Cola Company's sponsorship to continue to work legally in the United States.
Advertising Technologies, AI Governance, AI Risk Management, Algorithms, Artificial Intelligence (AI), Contract Negotiations, Data Stewardship, Digital, MarTech \& AI, Generative AI, Governance Model Design, Marketing Systems, Media Governance, Media Investment, Media StrategiesPay Range:
United States of America: 218,800 USD \- 247,200 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:
50*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:
20*Long\-term Incentive reference value is a market\-based competitive value for your role.*
Location(s):
United States of AmericaCity/Cities:
AtlantaTravel Required:
00% \- 25%Relocation Provided:
YesJob Posting End Date:
August 28, 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:United States of America: 0 USD \- 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:50
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:20
Long\-term Incentive reference value is a market\-based competitive value for your role.
Salary Context
This $218K-$247K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At The Coca-Cola Company, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($233K) sits 8% above the category median. Disclosed range: $218K to $247K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
The Coca-Cola Company AI Hiring
The Coca-Cola Company has 11 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in Atlanta, GA, US. Compensation range: $115K - $247K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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