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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI/ML Software Engineer - Gateway & Edge AI - **Company:** Qualcomm - **Location:** San Diego, CA, United States (Remote available) - **Salary:** $109,400 - $164,200 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, C++ (Programming Language), Cloud Computing, Profiling, Computer Programming, Computer Engineering, Data Structures, Software Debugging, Linux, Embedded Software, Python (Programming Language), Machine Learning, Network Diagnostics, Network Administration, Performance Tuning, Software Deployment, Systems Integration, Wi-Fi Technology, Pytorch, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Deep Learning, Model Validation, Caching, Reliability of Systems, Generative AI, Agentic-AI, Discretization, GGUF, Information Technology, Low Latency, Optimization Algorithms, ONNX (Open Neural Network Exchange) Format, HuggingFace, Hardware Acceleration, Machine Learning Operations, Model Inference, Network Optimization - **Published:** November 13, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=60e5c4b35f0e1be4 ## About the Role Qualcomm is looking for a highly motivated AI/ML Software Engineer to develop next-generation AI capabilities for gateway and edge products. This role will focus on applying Generative AI, LLMs/SLMs, agentic AI, and traditional machine learning to create intelligent networking experiences and services. The ideal candidate combines 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 to enable intelligent diagnostics, network optimization, security, automation, and user experiences., * Bachelor's degree in Computer Engineering, Computer Science, Electrical Engineering, or related field., * Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, AI/ML, or a 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., * 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. * ## Description + 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.