Principal Engineer

Company:

Qualcomm Technologies, Inc.

Job Area:

Engineering Group, Engineering Group  Machine Learning Engineering General

Summary:

Qualcomm’s Computer Vision Systems team is building the intelligence behind the world’s most advanced Snapdragon-powered devices from next-generation mobile phones to autonomous vehicles, IoT, robotics, and immersive AR/VR platforms. We are looking for a Machine Learning Engineer specializing in developing computer vision algorithms in the following domains: optical flow, depth estimation, visual tracking, multi-view geometry, visual odometry, SLAM, and 3D scene reconstruction .

This role is ideal for someone who thrives at the intersection of cutting-edge computer vision and deep learning, with strong hardware/software implementation experience.

Minimum Qualifications:

Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 8+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.

ORMaster's degree in Computer Science, Engineering, Information Systems, or related field and 7+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.

ORPhD in Computer Science, Engineering, Information Systems, or related field and 6+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.

What You’ll Do Algorithm & system implementation:
Research the latest trends in domain-specific computer vision, and design and develop models for real-world applications.

End-to-end ownership:
Train and optimize state-of-the-art machine learning and neural network methodologies; build and maintain training pipelines; work with and create very large datasets and evaluation benchmarks and integrate models into larger systems.

Leverage expert ML knowledge to extend training/runtime frameworks and model-efficiency tools with new features and optimizations; deploy models on Qualcomm Snapdragon platforms for real-time, on-device performance.

Analyze bottlenecks in end-to-end use cases and ML/AI workloads on Qualcomm hardware/software stacks via simulation and on-device characterization.

Own technical direction across projects, influence system-level architecture, and drive solutions from research through production deployment.

Serve as a technical lead for teams developing, adapting, and prototyping ML solutions; review and help write proposals and roadmaps for subsystems of complex products and features.

Act as a technical expert in ML model architecture and partner with hardware engineers to influence silicon design.

Preferred Qualifications Master's degree in Computer Science, Engineering, Information Systems, or related field.
5+ years of experience with ML frameworks (e.g., Tensor Flow, Caffe/Caffe2, PyTorch, Keras).5+ years of experience with low-level interactions between operating systems (e.g., Linux, Android, QNX) and hardware.
5+ years of experience in embedded system development and optimization applied to a specific ML problem domain (e.g., computer vision, perception, multimedia).5+ years of experience with one or more programming languages suitable for machine learning (e.g., Python, R, C, C++).5+ years of experience using statistics and probability (e.g., conditional probability, Bayesrule).4+ years in a technical leadership role, with or without direct reports (only applies to positions with direct reports).Experience

working in a large, matrixed organization. Experience in a role requiring interaction with senior leadership (e.g., Sr. Director and above).Experience working and communicating cross functionally in a team environment.

Developed 1+ novel machine learning architecture(s).Overall 10+ years of experience in AI/ML (focused on computer vision) algorithm development, commercialization. Proven track record architecting and shipping systems‑level AI solutions that combine application, runtime, and platform considerations (performance, power, memory, cost).On-device ML deployment knowledge including: quantization (INT8/FP16), pruning/distillation, profiling, memory/power budgeting, heterogeneous compute…