Software Engineer, AI Engineer (Applied​/Software), Machine Learning​/ ML Engineer

Company

Qualcomm Technologies, Inc.

Engineering Group >
Machine Learning Engineering

As a leading technology innovator, Qualcomm pushes the boundaries of what’s possible to enable next‑generation experiences and drives digital transformation to help create a smarter, connected future for all. As a Qualcomm Machine Learning Engineer, you will create and implement machine learning techniques, frameworks, and tools that enable the efficient discovery and utilization of state‑of‑the‑art machine learning solutions over a broad set of technology verticals or designs.

Qualcomm Engineers collaborate with cross‑functional teams to enhance the world of mobile, edge, auto, and IoT products through machine learning hardware and software.

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

• Master’s degree or PhD in Computer Science, Electrical/Computer Engineering, Robotics, or a related field with specialization in edge AI, computer vision, or embedded ML.
• 5+ years of experience with performance‑critical programming in C++, Python, including hardware‑aware optimization.
• 5+ years of experience with modern ML frameworks such as PyTorch, ONNX Runtime, TensorRT, TVM, OpenVINO, or Qualcomm’s AI toolchain including SNPE, QNN.
• 3+ years of experience developing real‑time edge AI systems with emphasis on vision, multimodal perception, and sensor fusion.
• Strong background in applied statistics, probabilistic modeling, and evaluation of ML systems under real‑world constraints such as latency, thermal limits, and bandwidth.
• Familiar with FFmpeg, GStreamer with solid knowledge of video codec and streaming technologies.
• Experience with computer vision and intelligent video analytics, including object detection, tracking, re‑identification, camera geometry and calibration, and cross‑camera association.
• Experience working in large cross‑functional organizations involving hardware, firmware, cloud, and product teams.
• Experience leading technical initiatives, mentoring engineers, or driving architectural decisions.
• Experience presenting technical strategy or results to senior leadership.

• Lead the design, development, and optimization of edge AI systems for real‑time video analytics, spanning model architectures, inference pipelines, and runtime frameworks deployed on AI camera and embedded platforms.
• Develop and integrate advanced computer vision and video analytics algorithms to deliver robust, production‑grade AI cameras and edge computer vision solutions.
• Design and optimize real‑time video processing pipelines, leveraging FFmpeg, GStreamer, and streaming protocols to handle high‑throughput, low‑latency video ingestion, preprocessing, inference, and post‑processing.
• Apply and evaluate machine learning techniques under real‑world constraints, incorporating system‑level considerations such as bandwidth, compute budget, memory footprint, thermal limits, and end‑to‑end latency.
• Prototype, validate, and product ionize novel ML solutions aligned with product roadmaps, transforming research concepts into reliable customer‑facing features.
• Lead experimental design, model training, benchmarking, and validation, establishing metrics, evaluation frameworks, and best practices to ensure model accuracy, robustness, and system performance at scale.
• Provide technical leadership across the organization, mentoring engineers, reviewing designs, and driving architectural decisions that shape the long‑term evolution of the ML and edge AI platform.
• Communicate technical strategy, trade‑offs, and results effectively to cross‑functional…