About the job
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.
Responsibilities
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.
Qualifications
Minimum
• 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.
OR
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.
OR
PhD in Computer Science, Engineering, Information Systems, or related field and 6+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
Preferred
Master's degree in Computer Science, Engineering, Information Systems, or related field.
5+ years of experience with ML frameworks (e.g., TensorFlow, 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, Bayes rule).
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 Mindset with Product Focus. Ability to translate research ideas into deployable systems. Comfortable reading and implementing from academic papers. Experience balancing innovation vs. production constraints
Strong software engineering foundations (Python/C++), containerization, AI accelerators, and profiling tools; fluency with modern inference/runtime stacks.
Model/system benchmarking and E2E evaluation (latency/accuracy/cost/power), testing, and operations for AI at the edge.
Background with Qualcomm AI platforms and heterogenous acceleration; familiarity with on-device inference and memory/power budgeting.
Domain exposure in one or more verticals: mobile, AR/VR, robotics, automotive, IoT.