AI Algorithms/Software Engineer

Job description

Mythic is building the future of AI computing with breakthrough analog technology that delivers 100× the performance of traditional digital systems at the same power and cost. This unlocks bigger, more capable models and faster, more responsive applications—whether in edge devices like drones, robotics, and sensors, or in cloud and data center environments. Our technology powers everything from large language models and CNNs to advanced signal processing, and is engineered to operate from –40 °C to +125 °C, making it ideal for industrial, automotive, aerospace, and defense. We’ve raised over $100M from world-class investors including Softbank, Threshold Ventures, Lux Capital, and DCVC, and secured multi-million-dollar customer contracts across multiple markets. ### The AI Engineering team - Builds software pipelines that adapt neural networks (such as Hugging Face, Ultralytics, or custom models) for deployment on Mythic’s hardware. - Develops advanced quantization-aware and analog-aware retraining algorithms leveraging PyTorch and ONNX. - Hardens networks to analog effects via advanced network regularization. - Models analog effects and their impact on network performance. - Works cross-functionally to validate and debug hardware. - Contributes to the co-design of next-generation hardware. - Brings up and customizes neural networks. ### Here's what you will do - Optimize Mythic’s analog-aware software toolchain for network accuracy, latency, and ease-of-use. - Design algorithms and tools for Mythic’s neural network conversion pipeline. - Build high-fidelity, computationally-efficient hardware models. - Contribute to silicon bring-up, debugging, and validation. - Improve software through refactoring, testing, documentation, and other engineering best practices. - Stay current with advances in deep learning research and neural network frameworks. ### Here's the background you need to have - Bachelor's degree in Computer Science, Mathematics, or a related field. - 5+ years of software experience in a production environment. - Experience working on complex problems with algorithm-heavy code. - Commitment to quality and engineering excellence. - Strong communication skills. ### The following would be nice to have - MS/PhD in Computer Science, Mathematics, or related field. - Hands-on experience with modern neural network frameworks. - Familiarity with state-of-the-art neural network architectures. - Experience training neural networks with hardware-aware techniques, including quantization, pruning, or model-size limitations. - Experience with MLOps practices, including model versioning, CI/CD pipelines for ML, model deployment, and monitoring. - Experience owning critical APIs with a large user base. - Contributions to open-source software.