As a Machine Learning Engineer at Qualcomm, you will be at the forefront of technology innovation, creating and implementing machine learning techniques and tools. You will collaborate with cross-functional teams to enhance mobile, edge, auto, and IoT products through advanced machine learning solutions. The role requires a strong background in hardware and software engineering, with a focus on optimizing machine learning frameworks. You will also be responsible for developing novel machine learning solutions and conducting experiments to evaluate models.

• Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 4+ years of relevant work experience, or a Master's degree with 3+ years, or a PhD with 2+ years.
• 5+ years of experience with machine learning frameworks such as Tensor Flow, Caffe, or Pytorch.
• 5+ years of experience in embedded system development and optimization related to machine learning.
• 5+ years of experience with programming languages suitable for machine learning, including Python, R, C, or C++.
• 5+ years of experience using statistics and probability.
• 3+ years of experience working in a large matrixed organization.
• 2+ years of experience with low-level interactions between operating systems and hardware.
• 1+ year in a technical leadership role or experience interacting with senior leadership.

• Leverage advanced machine learning knowledge to enhance training or runtime frameworks.
• Model, architect, and develop advanced machine learning hardware for inference or training solutions.
• Develop optimized software to enable AI models deployed on hardware.
• Apply machine learning techniques into products and AI solutions for customer use.
• Prototype novel machine learning solutions aligned with product roadmaps.
• Oversee and conduct experiments to train and evaluate machine learning models.

• Qualcomm offers competitive compensation, annual bonuses, stock programs, comprehensive healthcare coverage, retirement plans, wellness programs, parental leave, flexible work options, and professional development opportunities.