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About This Role
*Role Overview:*
We’re looking for a hands-on technical leader to architect, fine-tune, and deploy on-device small language models (SLMs) for consumer security at scale. You’ll lead a focused team of 3–5 senior engineers while remaining deeply involved in the code and technical architecture.
Your core responsibility is building high-performance, privacy-preserving AI models that run directly on user devices (Mac, iOS, Android, Linux). You’ll own model optimization, fine-tuning for tool-use accuracy, evaluation frameworks, and cost-aware deployment strategies. While you won’t own the agent orchestration platform itself, you’ll work closely with it to ensure models behave correctly in multi-turn conversations and make reliable tool-calling decisions.
This role sits at the intersection of edge ML, applied LLMs, and production engineering. Success requires navigating real-world tradeoffs: latency vs. capability, privacy vs. accuracy, on-device vs. cloud execution, and cost vs. performance.
This is not a traditional director role. You’ll spend 60%+ of your time on technical architecture and implementation, with the remainder focused on mentoring senior engineers and setting technical direction.
This is a Hybrid remote position located in a hub location of Frisco, TX or San Jose, CA. You will be required to be onsite on an as-needed basis, typically 1-4 days per month. We are only considering candidates within a commutable distance to this location and are not offering relocation assistance at this time.
About the role:
- Design and deploy small language models optimized for on-device inference (Mac, iOS, Android, Linux)
- Lead model optimization efforts including quantization, pruning, distillation, and efficient inference pipelines
- Fine-tune models to improve tool selection accuracy and conversational behavior in security-focused workflows
- Build evaluation frameworks to measure model efficacy, tool-calling accuracy, conversation quality, and safety in production
- Create synthetic data and workflow simulations to train and validate security-relevant conversations
- Partner closely with agent orchestration systems to optimize multi-turn dialogue behavior and state handling
- Implement cost-optimization strategies such as intelligent on-device vs. cloud routing, prompt caching, batching, and token efficiency
- Integrate cloud-based LLMs when deeper reasoning or broader context is required
- Build production ML systems that detect threats and protect users directly on-device
- Set technical standards and architectural direction for AI/ML across the security platform
- Mentor principal engineers and architects while remaining hands-on
About you:
- 10+ years of software engineering experience, with 5+ years focused on ML/AI
- Proven experience shipping ML models to production with transferrable skills to deploy these on edge or mobile platforms
- Experience with conversational AI systems and tool/function-calling architectures
- Strong Python and systems programming skills (C++ or Rust) for performance-critical code
- Deep expertise in model optimization (INT4/INT8 quantization, pruning, distillation)
- Hands-on experience with PyTorch and at least one edge deployment framework (TensorFlow Lite, CoreML, ONNX Runtime, or llama.cpp)
- Experience building evaluation and benchmarking frameworks for ML systems
Preferred:
- Experience applying ML systems in security, safety, or other adversarial domains
- Master’s degree in CS, ML, or a related field (or equivalent practical experience)
#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.
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 26,159 AI roles we're tracking, AI Software Engineer positions make up 2% of the market. At McAfee, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $252,000 based on 337 positions with disclosed compensation. Director-level AI roles across all categories have a median of $230,600.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
McAfee AI Hiring
McAfee has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Frisco, TX, US.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 $145,600. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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