Interop Labs
Interop Labs is a company focused on the development of interoperability technology for Web3. As the initial developer of the Axelar network, a programmable Web3 interoperability platform, it aims to scale next-generation internet applications to a global user base. The Axelar network is designed with a focus on programmability, security, and scalability, allowing developers to build applications that can run on all blockchains seamlessly. Interop Labs is committed to innovation and excellence, with a team comprising experts in distributed systems, cryptography, and technology, hailing from prestigious institutions like MIT, Google, and Consensys. The co-founders were instrumental in the foundation of Algorand, further evidencing their expertise in blockchain technology.
blockchain • cryptography • interoperability • web3
11 - 50 employees
🌐 Web 3
Machine Learning Runtime Optimization Engineer - Mac/Edge Devices
February 18
Web3
Interop Labs
Interop Labs is a company focused on the development of interoperability technology for Web3. As the initial developer of the Axelar network, a programmable Web3 interoperability platform, it aims to scale next-generation internet applications to a global user base. The Axelar network is designed with a focus on programmability, security, and scalability, allowing developers to build applications that can run on all blockchains seamlessly. Interop Labs is committed to innovation and excellence, with a team comprising experts in distributed systems, cryptography, and technology, hailing from prestigious institutions like MIT, Google, and Consensys. The co-founders were instrumental in the foundation of Algorand, further evidencing their expertise in blockchain technology.
blockchain • cryptography • interoperability • web3
11 - 50 employees
🌐 Web 3
📋 Description
• Looking for a Machine Learning Runtime Optimization Engineer to work on an innovative project. • Focus on backend optimizations for ML runtimes, including hardware acceleration, and inference speed improvements.
🎯 Requirements
• 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.
🏖️ Benefits
• Unlimited time off throughout the year to rest and recharge. • Competitive compensation with stock options.
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