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About This Role
Overview
The AI Engineer \- Cyber Programs is The Coca\-Cola Company's dedicated adversarial testing specialist for AI systems. This role systematically probes the Company's AI models, AI\-enabled applications, and AI infrastructure for vulnerabilities, ensuring that AI deployments across the enterprise are resilient against adversarial manipulation, misuse, and emerging attack techniques. The focus is on AI\-specific adversarial testing — not traditional network or application penetration testing — covering risks such as prompt injection, jailbreaking, data poisoning, model exfiltration, training data leakage, and abuse of agentic AI capabilities.
This is a senior individual\-contributor role that combines deep expertise in AI/ML systems with offensive security tradecraft applied to the AI domain. The role designs and executes AI cyber\-program engagements, develops testing methodologies and tooling, and delivers clear, actionable findings that enable the Company to harden its AI systems before and after deployment. The role partners with AI governance, development teams, and external specialists (including consultants and bug bounty programs) to ensure comprehensive adversarial coverage of the Company's AI portfolio.
Key Responsibilities
AI Adversarial Testing \& Red Teaming
- Plan and execute adversarial testing engagements against the Company's AI models, LLM\-based applications, agentic AI systems, and AI infrastructure.
- Test for AI\-specific vulnerabilities including prompt injection, jailbreaking, output manipulation, data poisoning, model inversion, training data extraction, membership inference, and adversarial evasion.
- Evaluate the security of AI system architectures, including model hosting, API exposure, data pipelines, fine\-tuning workflows, and agent tool\-use chains.
- Assess risks introduced by third\-party AI components, vendor\-embedded AI, and AI\-enabled SaaS products.
Methodology \& Tooling Development
- Develop and maintain AI red team methodologies, playbooks, and testing frameworks aligned with NIST AI RMF, MITRE ATLAS, OWASP Top 10 for LLMs, and emerging industry standards.
- Build and maintain custom tooling, scripts, and automation for AI adversarial testing, including prompt fuzzing, model probing, and automated vulnerability scanning of AI systems.
- Evaluate and integrate open\-source and commercial AI security testing tools (such as Garak, PyRIT, or similar) into the testing workflow.
- Stay current with the latest AI adversarial research, published attacks, and defensive techniques.
AI Security Controls Assessment
- Assess the effectiveness of AI security controls, including input/output filtering, content moderation, guardrails, access controls, logging, and monitoring.
- Evaluate human oversight mechanisms, model governance processes, and incident response readiness for AI\-specific security events.
- Test the resilience of AI systems under adversarial conditions, including coordinated multi\-step attacks and advanced jailbreak chains.
- Recommend proportionate controls and mitigations based on testing results, risk severity, and the Company's AI risk appetite.
External Partnerships \& Coordination
- Coordinate with external consultants, specialized AI security firms, and bug bounty programs for AI\-specific adversarial assessments.
- Engage with the AI security research community, industry working groups, and standards bodies to stay current and contribute the Company's perspective.
- Partner with AI governance, development teams, and business units to ensure testing priorities align with the Company's AI deployment roadmap.
Reporting \& Knowledge Building
- Produce clear, actionable adversarial testing reports with findings, evidence, risk ratings, and remediation guidance for technical and executive audiences.
- Maintain a knowledge base of AI vulnerabilities, attack patterns, and testing results to inform future assessments and track remediation progress.
- Brief Cyber Defense leadership and the CISO on AI threat trends, adversarial testing results, and the Company's AI security posture.
Qualifications
- Minimum 8–12 years of progressive cybersecurity experience, with at least 3–5 years focused on AI/ML security, adversarial testing of AI systems, or applied AI research with a security focus.
- Demonstrated hands\-on experience conducting adversarial testing of AI systems, including large language models, machine learning models, agentic AI, or AI\-enabled applications.
- Strong technical understanding of AI/ML systems, including model architectures, training pipelines, inference APIs, fine\-tuning, retrieval\-augmented generation (RAG), and agentic frameworks.
- Working knowledge of AI\-specific attack techniques, including prompt injection, jailbreaking, data poisoning, model inversion, training data extraction, and adversarial evasion.
- Familiarity with AI security frameworks and guidance, including NIST AI RMF, MITRE ATLAS, OWASP Top 10 for LLMs, and ISO/IEC 42001\.
- Experience with AI adversarial testing tools such as Garak, PyRIT, or similar, and the ability to develop custom testing tooling.
- Strong offensive security fundamentals, including experience with penetration testing, red teaming, or vulnerability research (applied to AI contexts).
- Excellent written and verbal communication skills, with the ability to produce clear adversarial testing reports for technical developers and executive leadership.
- Programming proficiency in Python and familiarity with ML frameworks (PyTorch, TensorFlow, or equivalent).
- Relevant certifications such as OSCP, GIAC (any), CISSP, or AI\-specific credentials are a plus.
Education
- Bachelor's degree in Computer Science, Cybersecurity, Data Science, Engineering, or related technical field required.
- Master's degree or advanced research experience in AI/ML security, adversarial machine learning, or a related discipline highly desirable.
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, Azure Devops, Business Intelligence Software, DevOps, GitHub, Microsoft Azure, Non Relational Databases, Object\-Oriented Programming (OOP), Python (Programming Language), Scala (Programming Language), Software Development, Software Engineering, Structured Query Language (SQL), Terraform (Software), Testing MethodologyPay Range:
United States of America: 152,000 USD \- 178,300 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.*
Location(s):
United States of AmericaCity/Cities:
AtlantaTravel Required:
00% \- 25%Relocation Provided:
NoJob Posting End Date:
August 14, 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: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.
Salary Context
This $152K-$178K range is below the median 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 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($165K) sits 23% below the category median. Disclosed range: $152K to $178K.
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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