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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Director, Software Engineering - AI ML Engineering - **Company:** McAfee, Inc. - **Location:** Frisco, TX, United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Android Software Development, Apple IOS, C++ (Programming Language), Program Optimization, Linux, Python (Programming Language), Machine Learning, Language Modeling, Tensorflow, Software Engineering, System Programming, Pytorch, Large Language Models, Deployment Automation, ONNX (Open Neural Network Exchange) Format, Machine Learning Operations, Artificial Intelligence Markup Language (AIML) - **Published:** July 23, 2026 - **Apply:** https://dejobs.org/x/x/C6F1432530684541A628E5B5B7C37DB0/job/ ## About the Role * 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) ## Description 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., * 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 ## Related Videos - [Docker network without Docker](https://www.wearedevelopers.com/videos/1418-docker-network-without-docker) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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