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Role Overview: McAfee is seeking a Lead Product Designer to help define the future of AI-powered consumer experiences.
As AI transforms how people work, communicate, create, and make decisions online, it is also reshaping the digital threat landscape. This role will lead the design of innovative experiences that help consumers navigate an increasingly complex world while leveraging advances in AI to deliver smarter, more proactive, and more personalized protection.
You will work at the forefront of emerging technology, designing experiences that address evolving customer needs, establish new paradigms for human-AI interaction, and bring innovative products from concept to market.
This is an opportunity for a design leader who thrives in ambiguity, enjoys shaping products from first principles, and views AI as both a transformative product capability and a powerful tool for accelerating design and innovation.
This is a hybrid role based in one of our hub locations i.e. Frisco, Texas; San Jose, CA; Newport Beach, CA or New York City. You will be expected to be in-office on an as-needed basis and will be working from your home office otherwise. Position Details:
About the Role
Define Future AI-Powered Experiences
- Lead the vision and design strategy for emerging AI-powered products and experiences.
- Explore how intelligent systems can help consumers make better decisions, reduce risk, and increase confidence.
- Translate complex technologies into intuitive, trustworthy customer experiences.
- Create new interaction models that balance automation, guidance, personalization, and user control.
- Establish experience principles that can scale across future generations of products and services.
Design for Trust, Transparency, and Safety
- Develop experiences that help users understand and trust AI-driven recommendations and actions.
- Define patterns for explainability, confidence levels, transparency, and user control.
- Create experiences that help customers navigate emerging digital risks and evolving online threats.
- Design systems that balance proactive protection with user autonomy.
- Advocate for responsible, ethical, and human-centered AI experiences.
Shape Human + AI Interaction Models
- Design workflows where users collaborate effectively with intelligent systems.
- Explore emerging paradigms including AI assistants, autonomous agents, recommendation systems, and adaptive experiences.
- Establish interaction patterns for guidance, intervention, automation, and decision support.
- Help define how intelligent systems should interact with people in moments of uncertainty, risk, or complexity.
Leverage AI Throughout the Design Process
- Integrate AI-powered capabilities into research, ideation, concept generation, prototyping, content creation, and design exploration.
- Experiment with emerging tools and workflows that accelerate product discovery and innovation.
- Help establish best practices for AI-enabled design and product development.
- Foster a culture of experimentation, continuous learning, and responsible AI adoption within the design organization.
Drive Product Strategy Through Design
- Translate customer insights, technology trends, and market opportunities into product vision and strategy.
- Partner closely with Product Management, Engineering, Research, Data Science, and business leaders to shape future direction.
- Facilitate strategic conversations and align stakeholders around customer-centered outcomes.
- Influence roadmap decisions through design leadership and strong customer advocacy.
Lead Through Influence
- Raise the quality of product thinking across the organization.
- Mentor and coach designers on systems thinking, AI-enabled workflows, and strategic design practices.
- Build alignment across cross-functional teams and executive stakeholders.
- Contribute to the growth and maturity of the design organization.
Required Qualifications
- 8+ years of product design experience, or equivalent experience shaping and delivering innovative digital products, platforms, services, or AI-driven experiences.
- Proven success leading complex initiatives from concept through launch.
- Strong expertise in interaction design, systems thinking, service design, human-centered design, and product strategy.
- Demonstrated ability to influence product direction and executive stakeholders.
- Experience working across multidisciplinary teams including Product, Engineering, Research, Data Science, and AI/ML practitioners.
- Strong ability to navigate ambiguity and define solutions in emerging technology spaces.
- Excellent communication, storytelling, facilitation, and stakeholder management skills.
- Proficiency with Figma and modern design systems.
AI-Specific Qualifications
- Demonstrated experience designing AI-powered products, intelligent systems, agentic experiences, or human-AI interactions.
- Strong understanding of AI capabilities, limitations, risks, and emerging user experience patterns.
- Experience designing experiences that help users understand, evaluate, and appropriately act on AI-generated recommendations, insights, or actions.
- Proven ability to leverage AI throughout the design lifecycle, including research synthesis, ideation, prototyping, content development, and evaluation.
- Deep curiosity about how AI is changing consumer behavior, trust, decision-making, and digital experiences.
- Passion for exploring how AI can transform both customer experiences and design workflows.
About You:
- Thinks in systems, not screens.
- Thrives in ambiguity and undefined problem spaces.
- Balances long-term vision with pragmatic execution.
- Uses customer insight and data to drive decision-making.
- Simplifies highly technical concepts into intuitive experiences.
- Understands how trust is built, maintained, and lost in digital products.
- Sees AI as both a product capability and a force multiplier for teams.
- Brings curiosity, strategic thinking, and a willingness to challenge conventional approaches.
#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 offer a variety of social programs, flexible work hours and family-friendly benefits to all of our employees.:* Bonus Program
- 401k Retirement
- Medical, Dental, Vision, Basic Life, Short Term Disability and Long-Term Disability Coverage
- Paid Parental Leave
- Support and Community Involvement
- 14 Paid Company Holidays
- Unlimited Paid Time Off for Exempt Employees
- 96 Hours of Sick Time and 120 Hours of Vacation for Non-Exempt Employees Accrued Each Year
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. Pay Range: The anticipated compensation for this position is USD $135,910.00/Yr. - USD $223,285.00/Yr. depending on experience and qualifications. Job Applicant Privacy Notice: Please click here to view and download the Job Applicant Privacy Notice, which applies to all McAfee job applicants who are residents of the state of California.
Salary Context
This $135K-$223K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($179K) sits 15% below the category median. Disclosed range: $135K to $223K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
McAfee AI Hiring
McAfee has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Jose, CA, US. Compensation range: $223K - $223K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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