Interested in this AI/ML Engineer role at McAfee?
Apply Now →Skills & Technologies
About This Role
*Role Overview:*
McAfee is a global leader in online protection. We are dedicated to making the digital world a safer place for everyone. We are seeking a highly experienced and hands\-on Technical Lead Software Engineer with deep expertise in cloud\-native architecture, agentic AI systems, strong background in full\-stack development, large\-scale internet services, and a proven track record of rapidly delivering AI/ML solutions into production. This is a unique opportunity to lead critical projects, mentor talented engineers, and shape the future of McAfee's innovative security products through fast, iterative delivery focused on business outcomes. We highly value experience gained at FAANG or other leading Big Tech companies.
This is a Hybrid Position located in one of our hub offices of Frisco, TX, San Jose, CA, Newport Beach, CA, New York, NY, and in Toronto or Waterloo, Canada. We are only considering candidates within a commutable distance to these locations.
You will be required to be onsite on an as\-needed basis; when not working onsite, you will work from your home office.About the Role:
- Lead the design, development, and deployment of scalable, resilient, and high\-performance software solutions with a focus on agentic AI/ML capabilities.
- Provide hands\-on technical leadership throughout the entire software development lifecycle, from concept and design to testing, deployment, and operational support.
- Architect and build full\-stack applications and services that operate on an internet scale, ensuring high availability and low latency on modern cloud platforms (AWS, Azure, GCP).
- Drive the technical vision and strategy for AI/ML\-powered features and products, translating business requirements into robust technical designs with rapid iteration and incremental delivery.
- Mentor and guide a team of software engineers, fostering a culture of technical excellence, innovation, collaboration, speed, and AI\-augmented productivity.
- Collaborate closely with product managers, data scientists, researchers, and other engineering teams to deliver impactful solutions aligned with business goals.
- Leverage AI\-assisted development tools and practices to accelerate delivery (GitHub Copilot, Claude Code, LLM\-powered workflows).
- Champion best practices in software engineering, including code quality, testing, CI/CD, and DevOps with a pragmatic, outcome\-driven approach.
- Stay current with emerging technologies and industry trends, particularly in AI/ML, agentic and LLM\-based systems, multi\-agent architectures, distributed systems, and cloud computing.
- Contribute to code reviews, design discussions, and architectural decisions with hands\-on involvement in implementation.
- Troubleshoot and resolve complex technical issues in production environments.
About You:
- 10\+ years of professional software development experience, with a significant portion in a technical leadership role.
- Extensive hands\-on experience in designing, building, and operating large\-scale, distributed internet services.
- Proven experience in developing and deploying agentic AI systems and AI/ML models into production environments. This includes familiarity with the end\-to\-end MLOps lifecycle and modern LLM\-based architectures.
- Strong proficiency in multiple programming languages such as Python, Java, Go, Rust, C\+\+ or similar.
- Deep understanding of full\-stack development, including front\-end technologies (e.g., JavaScript, TypeScript, Dart, React, Flutter) and back\-end technologies (gRPC, protobuf).
- Experience with cloud platforms (AWS, Azure, or GCP) and containerization technologies (Docker, Kubernetes) as a primary deployment model.
- Advanced Cloud \& K8s Infrastructure: Deep expertise in Kubernetes (Helm, ArgoCD), advanced cloud networking (Ingress, Calico), Zero Trust Security (OPA), Service Mesh (Istio/Linkerd), and eBPF (Cilium) for high\-performance observability and traffic management.
- Solid understanding of database technologies (SQL and NoSQL).
- Demonstrated use of AI productivity tools (GitHub Copilot, Claude, ChatGPT, etc.) to accelerate software development.
- Bias for action: proven track record of fast, iterative, incremental delivery over analysis paralysis.
- Business outcome orientation: focus on delivering value and measurable results, not just technical outputs.
- Excellent problem\-solving, analytical, and technical troubleshooting skills.
- Strong communication, interpersonal, and leadership abilities.
- Ability to thrive in a fast\-paced, agile environment with a strong hands\-on coding presence.
- Preferred: Contributions to open\-source projects.
- Preferred: Experience working at FAANG (Facebook, Amazon, Apple, Netflix, Google) or other leading Big Tech companies.
- Preferred: Experience with big data technologies (e.g., Data Bricks, Snowflake, Big Query, Spark, Hadoop, Kafka, etc.).
- Preferred: Experience in the cybersecurity domain.
\#LI\-Hybrid
*Company Overview*
McAfee is a leader in personal security for consumers. Focused on protecting people, not just devices, McAfee consumer solutions adapt to users’ needs in an always online world, empowering them to live securely through integrated, intuitive solutions that protects their families and communities with the right security at the right moment.
*Company Benefits and Perks:*
We work hard to embrace diversity and inclusion and encourage everyone at McAfee to bring their authentic selves to work every day. We’re proud to be Great Place to Work® Certified in 10 countries, a reflection of the supportive, empowering environment we’ve built where people feel seen, valued, and energized to reach their full potential and thrive.
We offer a variety of social programs, flexible work hours and family\-friendly benefits to all of our employees.
- Bonus Program
- Pension and Retirement Plans
- Medical, Dental and Vision Coverage
- Paid Time Off
- Paid Parental Leave
- Support for Community Involvement
We're serious about our commitment to diversity which is why McAfee prohibits discrimination based on race, color, religion, gender, national origin, age, disability, veteran status, marital status, pregnancy, gender expression or identity, sexual orientation or any other legally protected status.
The starting pay range for this position is $0\.00\-$0\.00\. McAfee takes into consideration an individual’s skillset, experience and location in making final salary determinations. For further details, please discuss with the Talent Acquisition Partner.
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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At McAfee, 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
McAfee AI Hiring
McAfee has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Jose, CA, US.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.