Interested in this AI/ML Engineer role at The Coca-Cola Company?
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About This Role
Digital products play a central role in how we create value for customers, support the teams who serve them, and shape the consumer experience.
Our product organization brings together small, empowered teams that move with clarity, speed,
and purpose, enabling digital to be a meaningful source of advantage across Coca\-Cola’s North America Operating Unit.
Our work spans customer journeys, service delivery, sales workflows, and the platforms that connect them. We are raising our standards for product craft and rebuilding the systems behind these experiences.
As a Tech Lead specializing in Machine Learning and Data Engineering, you will lead the technical direction for end\-to\-end ML capabilities that ship as part of our product, while also ensuring the data foundations (events, pipelines, feature tables, and governance) are reliable and scalable. You’ll partner with Product, Design, Data Science/Analytics, and platform teams to frame problems, define success metrics, and guide solutions from data modeling and feature engineering through model training, deployment, monitoring, and iteration. This is a hands\-on leadership role for engineers who can set standards, unblock teams, and drive execution across the ML and data stack without formal people\-management responsibilities.
What You WillWork On:
Build ML\-powered data products that model transaction drivers and surface optimized actions as insights to be embedded within integrated internal and external digital experiences that shape how our beverage brands activate across retail, foodservice, and digital channels. The success of our products is tied directly to measurable transaction lift at the point of sale, a primary objective of the North America Operating Unit and The Coca\-Cola Company as a whole.
How We Work
You’ll be part of a dedicated, cross\-functional team (Product, Design, Engineering) that is:
- Empowered to solve problems, not just build features
- Accountable for outcomes, not output
- Collaborative by default, from discovery through delivery
- Continuously learning, using data and customer insight to improve
Key Responsibilities
- Technical direction for a product ML domain: problem framing, approach selection, evaluation strategy, and iteration
- Data and feature foundations: event/telemetry definitions, transformation logic, feature/label tables, and training/serving consistency
- Production ML systems: deployment patterns (batch/online), model performance/latency tradeoffs, and operational readiness
- Quality and reliability: data quality checks, model monitoring (drift/performance), alerting, and runbooks
- Engineering standards: design reviews, code review quality, documentation, and reusable patterns for ML \+ data workflows
- Mentorship and enablement: coaching engineers through complex work and unblocking delivery across teams
Develop, Train \& Evaluate Models
- Build baselines and iterate on model approaches appropriate to the product problem (e.g., gradient boosting, deep learning, ranking)
- Lead feature engineering with strong data discipline: define entities and joins, validate labels, and ensure training/serving consistency
- Run experiments and evaluate models using sound methodology (train/validation splits, cross\-validation as appropriate, error analysis)
- Document findings and recommendations clearly for technical and non\-technical audiences
Deploy \&OperateModels in Production
- Deploy models to production (batch and/or real\-time) with attention to latency, reliability, and cost
- Implement monitoring for upstream data and feature freshness/quality, drift, and model performance; define alerting and response playbooks
- Automate repeatable training and evaluation workflows (versioning, reproducibility, and artifact tracking)
- Participate in incident response and post\-incident reviews when model behavior impacts customers or operations
- Establish reusable patterns for feature pipelines (batch/stream), backfills, and schema evolution; raise the bar through design reviews
- Define and reinforce standards for data governance and responsible ML (PII handling, access controls, data contracts, bias/fairness considerations)
- Partner with platform teams on the data stack (warehouse/lakehouse, streaming, orchestration) and MLOps tooling (feature stores, training infrastructure, deployment, monitoring)
WhatWe’reLooking For
- Applied ML fundamentals: Understands supervised learning, evaluation metrics, and common failure modes
- Strong programming skills: Comfortable in Python and writing production\-quality code (testing, readability, performance)
- Data intuition: Able to analyze datasets with SQL and/or Python, spot issues, and reason about bias/leakage
- Product mindset: Cares about measurable impact, guardrails, and user experience—not just model metrics
- Cross\-functional collaboration: Partners with Product, Data Science, and Engineering to ship and iterate on ML features
- MLOps\+ data platform fluency: Comfortable with deployment, monitoring, reproducibility, and the pipelines/warehouses/streams that feed models
Key Qualifications
- 6\+ years of experience in machine learning engineering, data engineering, or software engineering, including leading technical direction for ML/data systems
- Demonstrated ownership of model development and evaluation, including metric selection, error analysis, and experimentation discipline
- Strong engineering fundamentals in Python (and SQL) with production practices (testing, reviews, CI/CD); familiarity with ML frameworks (e.g., PyTorch/TensorFlow) and data tooling (e.g., Spark, dbt, Airflow/Dagster) is preferred
- Experience shipping and operating ML systems in production, including model monitoring, rollback/retraining strategies, and coordination with upstream data/feature pipelines
- Familiarity with data platforms (data warehouse/lakehouse concepts), and exposure to orchestration/ETL tools (e.g., Microsoft fabric, Airflow, dbt, Spark)
Preferred Qualifications
- Experience building product ML systems such as personalization, recommendations, ranking, forecasting, or NLP
- Experience with experimentation and measurement (A/B testing, uplift/impact analysis, online guardrails)
- Experience with feature pipelines or feature stores, and patterns for training/serving consistency
- Experience designing and operating data pipelines that power ML (batch and streaming), with clear SLAs for freshness and quality
- Experience with lakehouse/warehouse modeling for analytics and ML (dimensional/event models, backfills, schema evolution, data contracts)
- Demonstrated tech lead behaviors: driving design reviews, setting standards, mentoring engineers, and aligning stakeholders on tradeoffs
- Experience with model and data observability (drift detection, performance monitoring, dashboards/alerting)
- Familiarity with responsible AI and data privacy considerations (PII handling, access controls, model risk)
- Experience with production infrastructure (e.g., Docker/Kubernetes) or workflow tooling (e.g., Airflow, Dagster) used to run ML jobs
- Familiarity with modern engineering practices (CI/CD, testing, observability)
Education
- Bachelor’s degree in Computer Science, Engineering, or a related field
- Equivalent practical experience is equally valued
Who Thrives Here
- Enjoy leading through influence—turning ambiguous problems into clear ML \+ data plans and helping others execute
- Communicate clearly across Product, Data Science, Analytics, and Engineering—especially around definitions, tradeoffs, and risk
- Take pride in raising the bar: reliable models and data pipelines, strong documentation, and operational follow\-through
Who This Role Is Not For
This role may not be the right fit if you:
- Want to focus only on research prototypes or only on data pipelines (instead of owning end\-to\-end product ML systems)
- Avoid leading through influence (design reviews, alignment, mentorship) and prefer not to set or uphold technical standards
- Prefer to avoid operational responsibility for model and data health (monitoring, incidents, data quality/freshness, and continuous improvement)
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.
Agile Methodology, Atlassian JIRA, Business Processes, Business Process Modeling, Cloud Platform, Communication, Data Flow Diagram, DevOps, Digital Transformation, Enterprise Architecture Framework, Enterprise Content Management (ECM), Java (Programming Language), Kotlin Programming Language, Microsoft Office, Microsoft SharePoint, Mobile Applications, Object\-Oriented Programming (OOP), User Experience (UX)Pay Range:
United States of America: 171,000 USD \- 198,000 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:
30*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.*
Location(s):
United States of AmericaCity/Cities:
AtlantaTravel Required:
00% \- 25%Relocation Provided:
YesJob Posting End Date:
June 24, 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:30
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.
Salary Context
This $171K-$198K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 Required
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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $171K to $198K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
The Coca-Cola Company AI Hiring
The Coca-Cola Company has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, US. Compensation range: $198K - $198K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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