M/L Compiler Engineer

Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 2+ 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 1+ year of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience. OR PhD in Computer Science, Engineering, Information Systems, or related field. Master's degree in Computer Science, Engineering, Information Systems, or related field. 2+ years of experience with Machine Learning frameworks (e.g., Tensor Flow, Caffe, Caffe 2, Pytorch, Keras). 2+ years of experience in embedded system development and optimization with application to a specific problem domain in ML (e.g., NLP, multi-media). 2+ years of experience with one or more programming language suitable for machine learning (e.g., Python, R, C, C++) 2+ years of experience using statistics and probability (e.g., conditional probability, Bayes rule). 2+ years experience working in a large matrixed organization. 1+ year of experience with low level interactions between operating systems (e.g., Linux, Android, QNX) and Hardware. 1+ year of work experience in a role requiring interaction with senior leadership (e.g., Director and above). Applies Machine Learning knowledge to extend training or runtime frameworks or model efficiency software tools with new features and optimizations. Models, architects, and develops machine learning hardware (co-designed with machine learning software) for inference or training solutions. Develops optimized software to enable AI models deployed on hardware (e.g., machine learning kernels, compiler tools, or model efficiency tools, etc.) to allow specific hardware features; collaborates with team members for joint design and development. Assists with the development and application of machine learning techniques into products and/or AI solutions to enable customers to do the same. Develops, adapts, or prototypes complex machine learning algorithms, models, or frameworks aligned with and motivated by product proposals or roadmaps with minimal guidance from more experienced engineers. Conducts complex experiments to train and evaluate machine learning models and/or software independently. Works independently with minimal supervision. Decision-making may affect work beyond immediate work group. Requires verbal and written communication skills to convey information. May require basic negotiation, influence, tact, etc. Has a moderate amount of influence over key organizational decisions (e.g., is consulted by senior leadership to make key decisions). Tasks require multiple steps which can be performed in various orders; some planning, problem-solving, and prioritization must occur to complete the tasks effectively.