Company:
Qualcomm Technologies, Inc.Job Area:
Engineering Group, Engineering Group > Machine Learning ResearcherGeneral Summary:
The R&D work responsibility for this position focuses on the following
- Fundamental machine learning research in the area of ML combinatorial optimization and its applications, using tools like graph neural networks, learned message-passing heuristics, and reinforcement learning.
- Study of methods to integrate learning with search to solve important industrial problems such as efficient computation, resource allocation, and optimal network architecture.
- Research the relation of learning methods to classical optimization and search theory to develop new theoretical ideas to guide algorithm development.
- Identify new applications that benefit from the increased power to solve complex optimization problems.
- Apply solutions toward systems innovations for model efficiency advancement on device as well as in the cloud. This includes auto-ML methods for model compression, quantization, architecture search, and kernel/graph compiler/scheduling with or without systems-hardware co-design.
- Collaborate with departments across the organization and identify further areas where ML combinatorial optimization and related techniques may be applied to core problems.
- Review and assign projects throughout the team according to priorities, resources, deadlines, and deliverables. Plan and oversee research program across multiple technologies or product domains and develop complex innovative or patentable ideas.
Ideal candidates for this position will demonstrate the following:
- Bachelor's degree in Engineering, Information Systems, Computer Science, or related field.
- 10+ years Systems Engineering or related work experience.
- MS or PhD in Computer Science, Engineering, or related field
- Extensive experience in deep neural networks (e.g. Graph NN, CNN, RNN, Attention/Transformer) and deep reinforcement learning.
- Expertise in machine learning theory and optimization methods (e.g., Bayesian optimization, dynamic programming, reinforcement learning, stochastic tree search methods such as MCTS, unsupervised learning), as well as fundamentals in classical optimization problems (e.g., shortest path, TSP, min-cut) and solutions (e.g., Djikstra, Held-Karp)
- Proficiency in designing, implementing and training DL/RL algorithms in high-level languages/frameworks (e.g. PyTorch, TensorFlow, Caffe). Designing the network for an embedded device is a plus.
- Track record of research excellence and high-quality publications (e.g. NeurIPS, CVPR, ICML, ICLR, SysML).
- Experience helpful in: Operations Research; ML for Network Optimization; Model compression / quantization / optimization for embedded devices; Neural Architecture Search / kernel optimization; Computer vision; Audio and speech / NLP; Deep Generative Models (VAE, Normalizing-Flow, ARM, etc);
Minimum Qualifications:
- Master's degree in Computer Engineering, Computer Science, Electrical Engineering, or related field and 10+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
PhD in Computer Engineering, Computer Science, Electrical Engineering, or related field and 8+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
- 6+ months of experience developing and/or optimizing machine learning models, systems, platforms, or methods.
Preferred Qualifications:
- PhD in Computer Engineering, Computer Science, Electrical Engineering, or related field.
- 10+ years experience with machine learning research related to new models, systems innovations, platforms, or methodology.
- 4+ years in a technical leadership role with or without direct reports (only applies to positions with direct reports).
- 4+ years of experience working in a large matrixed organization.
- 3+ years of work experience in a role requiring interaction with executive leadership (e.g., Vice President and above).
- 2+ publications at a machine learning conference.
- 2+ years managing operating budgets and/or project financials.
Principal Duties and Responsibilities:
- Leverages expert Machine Learning knowledge to influence and oversee the fundamental research to create new models or training methods in various technology areas (e.g., deep generative models, Bayesian deep learning, equivariant CNNs, Bayesian optimizations, reinforcement learning, unsupervised learning, and graph NNs).
- Directs and oversees systems innovations for model efficiency advancement on device as well as in the cloud, including auto-ML methods for the creation and optimization of efficient models (e.g., model compression, quantization, architecture search, and kernel/graph compiler/scheduling).
- Determines guidelines for performing platform research to enable new machine learning compute paradigm (e.g., compute in memory, on-device learning/training, edge-cloud distributed/federated learning, and quantum machine learning).
- Develops and publishes research findings in the form of presentations and conference papers.
- Partners with senior stakeholders across the business to create the strategy for research programs across multiple technologies or products related to machine learning and executes on research proposals of various levels of complexity.
- Drives innovation in ideas and solutions based on current and future industry trends.
- Sets strategies to develop, test, optimize, and document new or updated machine learning algorithms, models, and methods.
- Provides supervision to other supervisors/managers who are direct reports.
- Decision-making is critical in nature and highly impacts program, product, or project success.
- Requires verbal and written communication skills to convey highly complex and/or detailed information. May require strong negotiation and influence with large groups or high-level constituents.
- Develops and administers budgets, schedules, and performance standards for functional area within the prescribed budgetary objectives of the department.
- Has influence over the formulation and achievement of long-term business plans and objectives.
- Tasks often require multiple steps which can be performed in various orders; extensive planning, problem-solving, and prioritization must occur to complete the tasks effectively.
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EEO Employer: Qualcomm is an equal opportunity employer; all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or any other protected classification.
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Pay range and Other Compensation & Benefits:
The above pay scale reflects the broad, minimum to maximum, pay scale for this job code for the location for which it has been posted. Even more importantly, please note that salary is only one component of total compensation at Qualcomm. We also offer a competitive annual discretionary bonus program and opportunity for annual RSU grants (employees on sales-incentive plans are not eligible for our annual bonus). In addition, our highly competitive benefits package is designed to support your success at work, at home, and at play. Your recruiter will be happy to discuss all that Qualcomm has to offer – and you can review more details about our US benefits at this link.
If you would like more information about this role, please contact Qualcomm Careers.
