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# AI Engineer - Algorithm Evaluation & Agentic Systems

- **Company:** Apple Inc.
- **Location:** Sunnyvale, CA, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Computer Vision, Memory Management, Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, Language Modeling, Regression Testing, Software Safety, Video Editing, Reinforcement Learning, Pytorch, Large Language Models, Multi-Agent Systems, Deep Learning
- **Published:** August 14, 2026
- **Apply:** https://www.techcareers.com/job.asp?id=3353290237&tx=FR2724FFG&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

What We Value Production mindset: correctness, observability and maintainability Ability to reason about system-level tradeoffs, not just model performance Ability to balance experimentation speed with engineering rigor Comfort working in ambiguous problem spaces and defining metrics from first principles Clear communication of technical findings to both technical and non-technical audiences, * MS and a minimum of 3 years relevant industry experience

 * 3+ years of applied experience in Machine Learning, Computer Vision, or AI System Evaluation
 * Solid ML Foundation: Deep understanding of core Machine Learning principles, including probability, statistics, data distributions, and model bias/variance. You can apply statistical rigor to ensure evaluation metrics are meaningful and reliable.
 * Computer Vision Expertise: Deep theoretical and practical understanding of Computer Vision (CV) and Vision-Language Models (VLMs). You must understand how Vision Transformers (ViTs), spatial-temporal modeling, and image/video processing work under the hood to effectively evaluate them.
 * Advanced Evaluation Skills: Proven track record of defining robust metrics/KPIs and designing rigorous evaluation frameworks for generative AI or foundation models. Deep experience with custom benchmark creation, automated regression testing, LLM/VLM-as-a-judge methodologies, and human-in-the-loop evaluation.
 * Agentic Systems: Experience building and evaluating LLM/VLM-powered agents, including tool use, multi-step reasoning, planning, and memory management workflows.
 * Failure Analysis: Strong intuition for probing ML models to discover edge cases, hallucinations, and performance bottlenecks in constrained environments. Be able to translate findings into actionable improvement recommendations.
 * Engineering Excellence: Strong proficiency in Python and experience with deep learning frameworks (PyTorch) for running inference, extracting embeddings, and building scalable evaluation pipelines., * Demonstrated ability to lead technical evaluation strategies end-to-end, drive architectural decisions for testing infrastructure, and mentor engineers.
 * Strong foundation in statistics, including hypothesis testing, confidence intervals, and experimental design
 * Knowledge of reinforcement learning, planning, or decision-making systems
 * Experience evaluating multi-modal or multi-agent systems
 * Prior work on AI reliability, safety, or benchmarking

## Description

How do we ensure Apple's next-generation AI products are robust, safe, and truly intelligent? Join the DAQ team to help answer that. We are seeking an AI Engineer specializing in algorithm evaluation and agentic systems design for advanced computer vision and video understanding algorithms., Within the DAQ team, our core mission is to evaluate and elevate advanced visual technologies. As a key member of this group, you will lead the benchmarking and integration of state-of-the-art models for image and video understanding. Rather than focusing on core model training, you will apply your deep CV and ML expertise to rigorously test models in applied settings, uncover edge-case failure modes, and architect advanced agentic systems. If you are passionate about AI safety, robust evaluation, and building autonomous multi-modal workflows that bridge experimentation with production, we'd love to hear from you.


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