Experience with ML inference engines (ONNX Runtime, TensorRT, CoreML, etc.) and optimizing models for deployment.
Proficiency in Mac/Linux-based runtimes and experience with heterogeneous compute environments (CPU/GPU/NPUs).
Deep understanding of numerical optimization, compiler techniques, and low-level performance tuning.
Open to new graduates with a PhD in optimization, systems, machine learning, or related fields.
Axelar (https://axelar.network) has raised over $100 million from leading VCs including DCVC, Galaxy, Polychain, Dragonfly, Coinbase, and more. Its diverse partner ecosystem spans industry heavyweights such as Ripple, Circle, dYdX, Uniswap, JPMorgan, Deutsche Bank, Microsoft, and others—underscoring Axelar’s unique market position and opportunity in unifying stacks between traditional finance and decentralized ecosystems. Axelar protocol was founded by Sergey Gorbunov (MIT PhD, UWaterloo Professor) and Georgios Vlachos (MIT MSc, Math Gold Medalist), who previously helped to build and launch Algorand.
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