Job Description
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 profile
• 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.
Why us?
• Autonomous, distributed environment with the opportunity to work collaboratively in a diverse team worldwide.
• The scope to contribute to high-impact work and make a difference in a decentralized protocol.
• The chance to challenge yourself while learning heaps of stuff in the process.
• Unlimited time off throughout the year to rest and recharge.
• Competitive compensation with stock options, experiencing growth from the initial phase.
About us
Interop Labs builds interoperability technology.
Interoperability between blockchains is crucial technology infrastructure for the growth of Web3 and the advancement of internet technology as a whole. Interop Labs is the initial developer of Axelar Network - the programmable Web3 interoperability platform, scaling the next generation of internet applications to billions of users. Axelar network's key attributes are programmability, security and scalability.
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.
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 profile
• 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.
Why us?
• Autonomous, distributed environment with the opportunity to work collaboratively in a diverse team worldwide.
• The scope to contribute to high-impact work and make a difference in a decentralized protocol.
• The chance to challenge yourself while learning heaps of stuff in the process.
• Unlimited time off throughout the year to rest and recharge.
• Competitive compensation with stock options, experiencing growth from the initial phase.
About us
Interop Labs builds interoperability technology.
Interoperability between blockchains is crucial technology infrastructure for the growth of Web3 and the advancement of internet technology as a whole. Interop Labs is the initial developer of Axelar Network - the programmable Web3 interoperability platform, scaling the next generation of internet applications to billions of users. Axelar network's key attributes are programmability, security and scalability.
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.
💡 Quick Summary
Seeking a career-building opportunity? The Machine Learning Runtime Optimization Engineer (Mac/Edge Devices) position is now open for candidates interested in the Work from home Jobs sector. This role in Abee offers a professional environment and growth potential.
Requirement Snapshot: Candidates should possess basic communication skills, a proactive attitude, and the ability to work in a team. Experience in Work from home Jobs is a plus.