Machine Learning Research Engineer, Senior

Qualcomm • San Diego, CA

2d • $140,800 - $211,200

About The Position

Qualcomm AI Research seeks a senior machine learning research engineer to join a multidisciplinary team spanning software, hardware, and systems. The team develops machine-learning technology and solutions that balance performance with efficient use of power, memory, and computation, and works with business teams across mobile, compute, automotive, cloud, and IoT to deploy technology worldwide. The group also conducts and publishes research, ranging from foundational theory to methods for deployment on resource-constrained devices.

The role may involve applied or fundamental machine-learning research. Areas include model efficiency and compression, sparsity, quantization and implementation optimization; new models and training methods, including large language models, generative models, reasoning, agentic frameworks, and reinforcement learning; new computing paradigms such as compute-in-memory and distributed or federated learning; and advanced applications across computer vision, audio, speech, NLP, image, video, power management, wireless, graphics, and chip design. The engineer will also develop and test machine-learning software and compiler components for hardware accelerators, and design, implement, and train deep-learning or reinforcement-learning algorithms. The role emphasizes making advanced algorithms efficient on edge, mobile, and embedded platforms.

Requirements

• Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field and at least 2 years of hardware, software, or systems engineering or related work experience; or a master's degree in one of these fields and at least 1 year of related experience; or a PhD in one of these fields.
• PhD in AI, Computer Science, Engineering, Information Systems, or a related field, or a master's degree with at least 4 years of machine-learning research or related work experience.
• Strong background in computer science and deep-learning fundamentals.
• 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.

Nice To Haves

• Experience with LLM, LVM, or LMM models and other neural-network architectures.
• Excellent interpersonal, written, and oral communication skills.
• Experience with machine-learning accelerators, optimizing algorithms for hardware acceleration cores, or heterogeneous or parallel computing systems.
• Experience designing and developing generalized AI solutions, including retrieval and agentic systems.
• Experience in LLM reasoning or inference-acceleration research.
• Experience producing on-device AI models or exposure to mobile or edge-device environments.
• First-author publications at major AI conferences such as NeurIPS, ICML, or ICLR.

Responsibilities

• Drive systems innovations that improve on-device and cloud model efficiency, including model compression, sparsity, quantization, and joint optimization of models and target implementations for memory, speed, and power.
• Conduct fundamental machine-learning research to develop new models and training methods in areas such as large language models, deep generative models, reasoning models, agentic frameworks, and reinforcement learning.
• Perform platform research enabling new machine-learning compute paradigms, including compute-in-memory, on-device learning and training, edge-cloud distributed or federated learning, quantum machine learning, and causal or language-based reasoning.
• Create machine-learning models for advanced use cases that achieve state-of-the-art performance across areas such as computer vision, audio, speech, NLP, image, video, power management, wireless, graphics, and chip design.
• Design, develop, and test machine-learning frameworks that optimize models for efficient execution on edge devices.
• Research, design, develop, enhance, and implement components of machine-learning compilers for hardware accelerators.
• Design, implement, and train deep-learning and reinforcement-learning algorithms using high-level languages and frameworks.

Benefits

• Competitive annual discretionary bonus program.
• Opportunity for annual RSU grants, except for employees on sales-incentive plans.
• Competitive benefits package.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree