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About This Role
Overview:
PepsiCo’s Global Application Security Program integrates security into software development at enterprise scale. As AI becomes a core component of enterprise applications, our mission expands to ensure AI systems are designed, developed, deployed, and operated securely by default.
This role serves as a senior technical contributor responsible for designing, implementing, and advancing AI Application Security capabilities. The ideal candidate brings deep technical expertise across AI systems, application security, and cloud\-native architectures to solve complex security challenges and enable secure AI adoption across the enterprise.
Responsibilities:
- Design and implement advanced security controls for AI\-enabled applications, including LLMs, AI agents, RAG pipelines, MCP servers, and AI workflows.
- Perform complex AI application threat modeling and security architecture assessments for high\-risk systems.
- Design and implement reusable AI security capabilities, libraries, frameworks, and automation to improve security at scale.
- Develop security controls for prompts, agent memory, tool execution, model interactions, identity, authorization, and sensitive data protection.
- Research, prototype, and evaluate emerging AI security technologies, attack techniques, and defensive capabilities.
- Identify and mitigate AI\-specific threats, including prompt injection, indirect prompt injection, model poisoning, jailbreaks, insecure tool execution, excessive agency, sensitive data disclosure, and AI supply chain risks.
- Design and integrate AI security controls into enterprise AI platforms, CI/CD pipelines, and developer workflows.
- Develop reusable reference implementations, security patterns, and engineering guidance for AI\-enabled applications.
- Partner with development, platform engineering, architecture, and Data \& AI teams to solve complex AI security challenges.
- Conduct advanced security assessments, code reviews, and architecture reviews for AI\-enabled applications.
- Evaluate AI security tooling and recommend improvements to increase detection accuracy, automation, and operational efficiency.
- Develop technical documentation, implementation guides, and engineering standards for AI security capabilities.
- Contribute to AI security metrics and continuously improve platform effectiveness and security posture.
- Mentor junior engineers through technical coaching, design reviews, and knowledge sharing.
- Support AI security investigations, incident response, and complex vulnerability remediation.
- Participate in Agile planning and contribute to strategic Application Security initiatives.
- Participate in a 24/7 on\-call rotation, including weekends and holidays.
Compensation and Benefits:
- The expected compensation range for this position is between $93,500 \- $156,450\.
- Location, confirmed job\-related skills, experience, and education will be considered in setting actual starting salary. Your recruiter can share more about the specific salary range during the hiring process.
- Bonus based on performance and eligibility target payout is 10% of annual salary paid out annually.
- Paid time off subject to eligibility, including paid parental leave, vacation, sick, and bereavement.
- In addition to salary, PepsiCo offers a comprehensive benefits package to support our employees and their families, subject to elections and eligibility: Medical, Dental, Vision, Disability, Health, and Dependent Care Reimbursement Accounts, Employee Assistance Program (EAP), Insurance (Accident, Group Legal, Life), Defined Contribution Retirement Plan.
Qualifications:
Technical Skills:
- Advanced knowledge of secure software development, secure architecture, and application security engineering.
- Advanced experience performing application and AI threat modeling using frameworks such as STRIDE, PASTA, or equivalent.
- Deep understanding of the OWASP Top 10, OWASP Top 10 for LLM Applications, and secure software engineering practices.
- Strong experience designing and securing cloud\-native applications in AWS (preferred), Azure, or GCP.
- Strong proficiency in Python and/or Go.
- Experience integrating security into CI/CD pipelines and DevSecOps workflows.
- Deep understanding of modern AI architectures, including:
+ Large Language Models (LLMs)
+ AI Agents
+ Retrieval\-Augmented Generation (RAG)
+ Model Context Protocol (MCP)
+ Embeddings and Vector Databases
+ Tool Calling and Function Execution
+ AI Memory and Context Management
+ Multi\-Agent Systems
- Experience mitigating AI threats including prompt injection, model poisoning, jailbreaks, insecure tool execution, excessive agency, prompt leakage, sensitive data disclosure, and AI supply chain attacks.
- Experience evaluating AI security technologies such as Promptfoo, NVIDIA Garak, Protect AI, Lakera, HiddenLayer, Microsoft AI Security, or equivalent.
- Experience with AI development frameworks such as LangChain, LangGraph, Semantic Kernel, MCP, OpenAI SDK, Azure AI Foundry, Amazon Bedrock, or similar.
- Strong understanding of identity, authorization, API security, cryptography, secrets management, and runtime security for AI systems.
Non\-technical Skills:
- Strong written, verbal, and presentation skills.
- High integrity with sound judgment and accountability.
- Exceptional analytical, troubleshooting, and systems\-thinking abilities.
- Self\-motivated with a passion for solving complex technical problems.
- Strong collaboration and communication skills across engineering teams.
- Comfortable working in a fast\-paced, global environment with evolving technologies and ambiguity.
- Ability to perform effectively under pressure.
\>:
Our Company will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of the Fair Credit Reporting Act, and all other applicable laws, including but not limited to, San Francisco Police Code Sections 4901\-4919, commonly referred to as the San Francisco Fair Chance Ordinance; and Chapter XVII, Article 9 of the Los Angeles Municipal Code, commonly referred to as the Fair Chance Initiative for Hiring Ordinance.
All qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or disability status.
PepsiCo is an Equal Opportunity Employer: Female / Minority / Disability / Protected Veteran / Sexual Orientation / Gender Identity / Age
If you'd like more information about your EEO rights as an applicant under the law, please download the available EEO is the Law \& EEO is the Law Supplementdocuments. View PepsiCo EEO Policy.
Please view our Pay Transparency Statement.
Salary Context
This $93K-$156K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At PepsiCo, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($124K) sits 43% below the category median. Disclosed range: $93K to $156K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
PepsiCo AI Hiring
PepsiCo has 3 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in Plano, TX, US. Compensation range: $134K - $156K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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