AI/ML Software Engineer, Gateway & Edge AI

About the Role

Qualcomm is seeking an AI/ML Software Engineer to develop next-generation AI capabilities for gateway and edge products. This role focuses on applying Generative AI, LLMs/SLMs, agentic AI, and traditional machine learning to create intelligent networking experiences and services. The ideal candidate will combine strong software engineering fundamentals with hands-on AI/ML experience and an interest in deploying AI on resource-constrained edge platforms. The engineer will work across on-device, cloud, and hybrid AI architectures.

Key Responsibilities

• Design and develop AI/GenAI features and use cases for gateway and edge products, translating real-world networking problems into production-ready AI solutions.
• Develop AI-powered capabilities for network diagnostics, troubleshooting, optimization, security, automation, anomaly detection, and intelligent user experiences.
• Integrate and optimize LLMs, SLMs, and ML models for deployment on resource-constrained edge devices.
• Develop on-device, cloud, and hybrid AI solutions, selecting the appropriate execution model based on latency, privacy, compute, memory, power, and connectivity requirements.
• Build agentic AI and tool-calling capabilities that allow AI models to interact with device services, network telemetry, diagnostics, configuration, and other system functions.
• Develop reusable AI inference, model-serving, and model-management capabilities that make AI functionality easily consumable by gateway applications.
• Evaluate and optimize models for accuracy, latency, throughput, memory utilization, compute requirements, and power efficiency.
• Apply AI optimization techniques such as quantization, fine-tuning, RAG, prompt/context optimization, caching, and intelligent model selection/routing.
• Prototype emerging AI use cases, evaluate technical feasibility and product value, and transition successful concepts toward production.
• Develop AI observability and diagnostics to measure model behavior, inference performance, resource utilization, and system reliability.
• Collaborate with AI, networking, embedded software, cloud, application, and product teams to deliver production-quality AI features.
• Evaluate emerging technologies in GenAI, agentic AI, edge AI, multimodal AI, and AI runtimes and identify opportunities to apply them to future gateway products.

Requirements & Tech Stack

• Bachelor's degree in Computer Engineering, Computer Science, Electrical Engineering, or related field.
• Strong programming skills in C/C++ and/or Python.
• Strong understanding of software engineering fundamentals, data structures, algorithms, and system design.
• Understanding of machine learning, deep learning, and Generative AI fundamentals.
• Experience developing or integrating AI/ML models, LLMs, or AI inference frameworks.
• Good understanding of Linux systems development and debugging.
• Understanding of AI performance characteristics such as latency, throughput, memory utilization, model accuracy, and compute requirements.
• Strong analytical and problem-solving skills with the ability to debug issues across software, AI models, and system layers.

Preferred Qualifications

• Experience deploying AI models on embedded, edge, IoT, networking, or other resource-constrained devices.
• Experience with LLMs/SLMs, model inference, quantization, fine-tuning, RAG, prompt engineering, and context management.
• Experience with agentic AI, function/tool calling, MCP, or similar agent frameworks.
• Familiarity with AI runtimes and hardware acceleration across CPU, GPU, and NPU.
• Experience with model ecosystems and tooling such as PyTorch, ONNX, Hugging Face, GGUF, or equivalent frameworks.
• Understanding of networking, broadband gateways, Wi-Fi, telemetry, diagnostics, or network management.
• Experience profiling and optimizing AI workloads for latency, memory, compute, and power constraints.
• Experience taking AI capabilities from prototype through productization and production deployment.