Backed by NEA and Lux.
About the role
At Mythic, we foster a collaborative and respectful environment where people can do their best work. We hire smart, capable individuals, provide the tools and support they need, and trust them to deliver. Our team brings a wide range of experiences and perspectives, which we see as a strength in solving hard problems together. We value professionalism, creativity, and integrity, and strive to make Mythic a place where every employee feels they belong and can contribute meaningfully.
What they're looking for
- 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
More about this role
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.
- 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...
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