Staff Engineer, Application Engineering; AI

Position: Staff Engineer, Application Engineering (AI)
Company:Qualcomm Atheros, Inc.
Job Area:Engineering Group, Engineering Group >
Software Applications Engineering

General

Summary:

As a Staff Application Software Engineer in the WIN Customer Engineering team, you will lead the integration of state-of-the-art AI/ML capabilities into Qualcomm’s next-generation Wi-Fi 7 and Wi-Fi 8 Access Point platforms. You will bridge the gap between advanced AI research and commercial deployment, helping Tier-1 customers run optimized CNNs and LLMs directly on the edge device (AP/Router).

This role requires a unique blend of Machine Learning expertise (quantization, model optimization) and Embedded Systems knowledge (DDR profiling, Linux kernel) to ensure AI applications operate efficiently.

Key Responsibilities

1. Edge AI Model Development & Optimization

Model Optimization:
Lead the optimization of AI models (CNNs, Transformers, LLMs) for deployment on resource-constrained embedded targets. Utilize Quantization techniques (INT8/INT4) and pruning to fit models within limited memory (DDR) and compute budgets.

Hardware Acceleration:
Offload inference workloads to the Hexagon NPU (NSP) and DSP to maximize performance per watt, ensuring minimal impact on the host CPU.

Debug & Profiling: perform deep-dive debugging of accuracy loss during quantization and runtime inference failures.

2. Agentic AI & LLM Applications

Network Agents:
Develop "Agentic" workflows where local LLMs (e.g., Llama 3, Phi-3) analyze network telemetry to autonomously optimize Wi-Fi performance (e.g., "Gaming Mode" QoS tuning, Mesh steering) or assist with troubleshooting.

Edge Inference:
Implement pipelines for on-device Generative AI and Multi-modal models (Vision + Text) to enable smart sensing and security features on the Gateway.

3. System Performance & Integration

Resource Management:
Conduct rigorous CPU and DDR profiling to ensure AI workloads do not starve the networking stack (packet processing latency, throughput). Tune system memory interaction between the NPU, CPU, and Wi-Fi subsystems.

Integration:
Integrate AI inference engines (e.g., TFLite, ONNX Runtime, Qualcomm AI Stack) into the Open Wrt/Linux based SDK.

4. Customer Enablement & Strategy

Technical Leadership:
Serve as the AI subject matter expert for customers, guiding them on model selection, training pipelines, and deployment strategies for Qualcomm platforms.

Cloud Hybridization:
Architect solutions that balance edge processing with cloud-based model updates and scalability.

Minimum Qualifications:

• Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 4+ years of Software Applications Engineering, Software Development experience, or related work experience.
OR
Master's degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Software Applications Engineering, Software Development experience, or related work experience.
OR
PhD in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Applications Engineering, Software Development experience, or related work experience.

• 2+ years of experience with Programming Language such as C, C++, Java, Python, etc.

• 1+ year of experience with debugging techniques.

Preferred Qualifications:

• Master's degree in Electrical Engineering, Computer Engineering, Computer Science, or related field.

• Experience:
  
  5+ years of software engineering experience with a strong focus on Embedded AI/ML.

• AI/ML Expertise:
  Deep proficiency in PyTorch or Tensor Flow. Hands-on experience with CNNs (Object Detection, Classification) and LLMs (Transformers).

• Model Optimization:
  Expert knowledge of Model Quantization (Post-Training Quantization, QAT), model compression, and debugging accuracy issues.

• Systems Programming:
  Strong coding skills in C/C++ and Python. Experience with Linux user-space development, multi-threading, and memory management.

• Performance Profiling:
  Proficiency with profiling tools (e.g., perf, eBPF, hardware counters) to analyze DDR bandwidth, cache misses, and CPU load.

• Soft Skills:
  
  Excellent problem-solving abilities and communication skills to articulate complex AI concepts to networking engineers and…