Machine Learning Engineer

As Staff Machine Learning Engineer for our AI Research, you work directly with ML research teams to implement algorithms, conduct experiments, develop research-oriented software tools, as well as work together with both research and product teams to bring AI models to embedded devices.

Your role will involve rapid prototyping, large-scale experimentation and fast iteration, always emphasizing code quality, maintainability, and efficiency. You will also contribute to comprehensive system design and engineering efforts, facilitating the transition of research prototypes toward commercial deployment. Candidates should have strong experience with the Python and PyTorch tech stack and a solid understanding of model quantization techniques. Proficiency in C++ and Android development and hands-on experience working with embedded platforms is a big plus. Successful applicants will be creative, enthusiastic innovators who are equally comfortable with researching new technologies and implementing code for robust prototypes. Requirements

• Excellent Python programming skills demonstrated through relevant industry or academic experience
• Proven experience with machine learning and frameworks such as PyTorch and hands-on experience with training deep neural networks, generating and evaluating experimental results, and improving training pipelines
• Proven experience with embedded computing and/or the Android platform, and a solid understanding of C++
• Background in software development, incl. testing, debugging, and test-driven development
• Ability to work in a multi-site software organization, * Strong software design, development, and debugging skills combined with a solid foundation in AI and general ML techniques
• Proven hands-on experience evaluating and optimizing Generative AI workflows for accuracy, performance, and other key metrics
• Experience with optimization of algebraic operations in algorithms for HW cores
• Prior experience with ML model optimization frameworks and a familiarity with applying techniques such as quantization, pruning, etc.
• Familiarity with containerization tools, test frameworks and static analysis tools and ability to work with continuous integration infrastructure.

Education Requirements

• PhD or M.S. in computer science, electrical engineering, robotics, or a related field, or a B.S. with several years of employment in related fields.