Principal Engineer

Company:

Qualcomm Technologies, Inc.

Engineering Group, Engineering Group >
Machine Learning Engineering

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.

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

• 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.

• 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 (CPU/GPU/DSP/NPU).
• Research…