Staff Engineer, Machine Learning Engineering
- Santa Clara, CA
- Permanent
- Full-time
Machine Learning EngineeringGeneral Summary: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.Minimum Qualifications: • 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.Role responsibility can include both applied and fundamental research in the field of machine learning with development focus in one or many of the following areas:
- Conducts fundamental machine learning research to create new models or new training methods in various technology areas, e.g., large language models, deep generative models (transformers, state-space networks, diffusion networks, VAEs, etc.), reasoning models, agentic frameworks, and reinforcement learning techniques.
- Drives systems innovations for model efficiency advancement on device as well as in the cloud. This includes model compression, model sparsity, quantization optimization methods, plus unified optimization of models and their implementation on-target to balance memory, speed, and power.
- Performs advanced platform research to enable new machine learning compute paradigms, e.g., compute in memory, on-device learning/training, edge-cloud distributed/federated learning, quantum machine learning, causal and language-based reasoning.
- Creates new machine learning models for advanced use cases that achieve state-of-the-art performance and beyond. The use cases can broadly include computer vision, audio, speech, NLP, image, video, power management, wireless, graphics, and chip design.
- Design, develop & test software for machine learning frameworks that optimize models to run efficiently on edge devices. Candidate is expected to have strong interest and deep passion on making leading-edge deep learning algorithms work on mobile/embedded platforms for the benefit of end users.
- Research, design, develop, enhance, and implement different components of machine learning compiler for HW Accelerators.
- Design, implement and train DL/RL algorithms in high-level languages/frameworks.
- PhD in AI, Computer Science, Engineering, Information Systems, or related field, or MS with 4+ years of machine learning research, or related work experience.
- Strong background in computer science and deep learning basics
- Strong programming skills in Python and PyTorch. Proficiency in designing and implementing complex training and evaluation pipelines.
- Exceptional analytical, development, debugging and problem solving skills
- Experience with LLM, LVM, LMM models, and other neural network architectures.
- Excellent interpersonal, written, and oral communications skills
- Experience with machine learning accelerators, optimizing algorithms for hardware acceleration cores, working with heterogeneous or parallel computing systems.
- Design and develop generalized AI solutions, including retrieval and agentic systems.
- Experience in LLM reasoning or inference acceleration research Experience of on-device AI model production or exposure to mobile / edge device environments
- First author publications experience at major AI conferences, e.g., NeurIPS, ICML, and ICLR