- Location
- San Diego, CA,US, US
- Type
- Full-time
- Department
- Engineering
- Seniority
- Senior
- Experience
- 6+ years
- Education
- PhD
- Source
- Eightfold
Description
Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 4+ 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 3+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience. OR PhD in Computer Science, Engineering, Information Systems, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience. Master's degree in Computer Science, Engineering, Information Systems, or related field. 6+ years of experience with Machine Learning frameworks (e.g., Tensor Flow, Caffe, Caffe 2, Pytorch, Keras). 6+ years of experience in embedded system development and optimization with application to a specific problem domain in ML (e.g., NLP, multi-media). 6+ years of experience with one or more programming language suitable for machine learning (e.g., Python, R, C, C++) 6+ years of experience using statistics and probability (e.g., conditional probability, Bayes rule). 3+ years experience working in a large matrixed organization. 2+ years of experience with low level interactions between operating systems (e.g., Linux, Android, QNX) and Hardware. 1+ year in a technical leadership role with or without direct reports (only applies to positions with direct reports). 1+ year of work experience in a role requiring interaction with senior leadership (e.g., Director and above). Design, develop, and optimize features for ONNX Runtime Execution Provider, ExecuTorch Edge IR graph lowering stack, and LiteRT delegates. Research and recommend leading technologies related to the PyTorch and ONNX ecosystems, model architectures, graph lowering and optimization techniques, and quantization methods. Validate, analyze, and optimize the performance and accuracy of software through detailed testing of machine learning use cases. Debug complex issues, perform root cause analysis, and ensure high system reliability. Collaborate with cross-functional teams to deliver robust, scalable AI software solutions. Lead feature development and application of machine learning techniques into products and AI solutions, enabling customers to do the same. Contribute to a culture of technical excellence, knowledge sharing, and continuous improvement within the AI Software team. Participate in design and code reviews. Work independently with minimal supervision and provide guidance to junior team members. Decision-making will impact your direct area of work and the broader work group. Leverages advanced Machine Learning knowledge to extend training or runtime frameworks or model efficiency software tools with new features and optimizations. Models, architects, and develops advanced 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 hardware teams for joint design and development. Develops and applies machine learning techniques into products and/or AI solutions to enable customers to do the same. Develops, adapts, or prototypes novel machine learning solutions aligned with and motivated by proposals or roadmaps for complex products and working features. Oversees and conducts experiments to train and evaluate machine learning models and/or software. Works independently with minimal supervision.
