Machine Learning Engineer – Generative AI (LLM, RAG, Agents) – San Diego, California | Visa + Salary Insights

📍 Location: us

🏷 Type: Full-time

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Job Overview

This role at :contentReference[oaicite:0]{index=0} focuses on building next-generation Generative AI systems for production-scale environments. The position is designed for engineers with strong expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agentic AI systems. The ideal candidate is someone who can move beyond experimentation into robust deployment, optimization, and evaluation of AI systems that integrate external knowledge sources. This is a high-impact engineering role within a core AI team, requiring both research fluency and production engineering discipline.

📅 Job Timeline & Status

This role is in an active hiring phase, likely in early-to-mid candidate screening based on posting recency. Given Qualcomm’s competitive AI hiring pipeline and strong demand for LLM engineers, applicants should treat this as a high-urgency application. Roles involving Generative AI and RAG systems typically attract global applicants quickly, meaning early submissions have a measurable advantage in recruiter visibility.

🌍 Work Eligibility & Location

This is a US-based Onsite/Hybrid role with strong preference for candidates able to align with US engineering teams. International applicants with strong LLM/RAG experience may be considered under visa sponsorship pathways, especially for senior-level profiles.

💰 Salary Intelligence

This compensation band is highly competitive for Generative AI roles in the US semiconductor and edge AI ecosystem. With RSU grants and bonus structures, total compensation can exceed base salary significantly, especially for candidates with production-grade LLM deployment experience.

📊 Role Breakdown

This role centers on building scalable Generative AI systems that combine LLMs, vector databases, and RAG pipelines into production-ready architectures. Engineers are expected to design systems that improve model accuracy by integrating external knowledge sources and optimizing retrieval pipelines. A major focus is developing agent-based workflows capable of autonomous reasoning and task execution across distributed environments.

Approximately 30% of the role involves designing and implementing RAG architectures, ensuring semantic retrieval quality and low-latency inference. Another 25% focuses on fine-tuning LLMs using domain-specific datasets to improve performance for targeted applications. Around 20% is dedicated to building evaluation frameworks that measure hallucination reduction, retrieval precision, and response quality.

The remaining time involves integrating PyTorch, TensorFlow, and modern ML infrastructure tools into scalable pipelines deployed on AWS/GCP/Azure. Engineers will also collaborate with cross-functional teams to deploy models using microservices and containerized environments. Strong emphasis is placed on production reliability, observability, and continuous model improvement.

🧩 Required Skills & Fit

📈 Difficulty & Competitiveness

This is a Senior-level AI engineering role with significant depth in both research and production systems. Candidates are expected to demonstrate not only ML theory but also large-scale deployment experience. Competition is intense due to the combination of Generative AI specialization and Qualcomm’s strong global engineering brand.

🚀 Career Impact

This role offers strong long-term career acceleration into advanced AI systems engineering. Exposure to production-scale LLMs and agentic architectures positions candidates for future roles in AI research, infrastructure engineering, and applied machine learning leadership.

📋 Key Responsibilities

The engineer will design and deploy LLM-based systems integrated with external knowledge sources using RAG pipelines. Responsibilities include building scalable inference services, optimizing embedding models, and improving retrieval accuracy. A core part of the role involves fine-tuning large models for domain adaptation and ensuring stable performance under production constraints.

The role also requires building evaluation frameworks to assess hallucination rates, response relevance, and retrieval effectiveness. Engineers will implement agentic workflows capable of autonomous decision-making across multi-step tasks. Additionally, collaboration with infrastructure teams will ensure seamless deployment on AWS, GCP, or internal cloud systems using containerized microservices architectures.

🎯 Application Strategy

🧠 Application Optimization (Adaptive)

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🧠 Fit & Positioning Analysis

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📅 Application Signals

This role shows strong application urgency due to its recent posting and high-demand AI specialization. With increasing global competition in Generative AI engineering, early applicants are more likely to pass initial screening cycles. The absence of a strict deadline suggests rolling review, meaning positions may close without notice if hiring targets are met. Expect fast initial screening cycles followed by technical interviews within weeks for shortlisted candidates. Overall competition is extremely high due to LLM-focused scope and Qualcomm’s global brand presence.

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✅ Job Source & Verification

This listing is sourced directly from Qualcomm’s official careers platform, ensuring high source credibility. The information was reviewed and last verified on May 31, 2026. Compensation and role structure align with Qualcomm’s publicly stated engineering salary bands and AI hiring patterns, confirming strong reliability for applicants evaluating this opportunity.