About the role We are looking for a Machine Learning Runtime Optimization Engineer to work on an innovative project that redefines software. In this role, you will focus on backend optimizations for ML runtimes, including hardware acceleration and inference speed improvements. Your responsibilities Responsibilities include: Optimizing ML inference engines (ONNX Runtime, TensorRT, CoreML, etc.) and models for deployment. Working with Mac/Linux-based runtimes and heterogeneous compute environments (CPU/GPU/NPUs). Applying numerical optimization, compiler techniques, and low-level performance tuning. Your profile Experience with ML inference engines and model optimization. Proficiency in Mac/Linux-based runtimes. Deep understanding of numerical optimization and compiler techniques. Open to new graduates with a PhD in optimization, systems, machine learning, or related fields. Why us? Autonomous, distributed environment with the opportunity to work collaboratively in a diverse team worldwide. Scope to contribute to high-impact work and make a difference in a decentralized protocol. Chance to challenge yourself while learning. Unlimited time off throughout the year to rest and recharge. Competitive compensation with stock options, experiencing growth from the initial phase. #J-18808-Ljbffr Interop Labs
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