About the job
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.
Responsibilities
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 productionize 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 stakeholders and senior leadership, influencing product direction and execution.
Qualifications
Minimum
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. OR 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. OR PhD in Computer Science, Engineering, Information Systems, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
Preferred
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 framework 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.