AI Research Engineer – Kernel & Inference Optimization

🕒 May 19

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Tether.to

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2014

₿ Crypto

💳 Fintech

💸 Finance

Crypto • Fintech • Finance

Tether. to is a leading digital asset company that pioneers the use of stablecoins in the blockchain space. As the most widely adopted stablecoin, Tether tokens are designed to be pegged 1-to-1 with fiat currencies, offering a stable digital asset option for users. The platform facilitates these token transactions across multiple blockchains, enhancing cross-border transactions while maintaining transparency with daily records of total assets and reserves. Tether's initiatives include educational programs promoting digital asset usage, especially targeting regions like the Middle East, Turkey, and the Philippines. Tether thus positions itself as a disruptor in the traditional financial system by enabling a stable, efficient method of handling transactions in the digital currency world.

📋 Description

• Drive innovation in model serving and inference architectures for advanced AI systems. • Focus on optimizing model deployment and inference strategies. • Work on a wide spectrum of systems, from resource-efficient models to complex, multi-modal architectures. • Develop, test, and implement novel serving strategies and inference algorithms. • Engineer robust inference pipelines, establish performance metrics, and resolve bottlenecks in production environments. • Enable high-throughput, low-latency, low-memory footprint, and scalable AI performance that delivers tangible value.

🎯 Requirements

• A degree in Computer Science or related field. • Ideally PhD in NLP, Machine Learning, or a related field, complemented by a solid track record in AI R&D (with good publications in A* conferences). • Must have knowledge of Metal Shading Language (MSL). • Proven experience in low-level kernel optimizations and inference optimization on mobile devices is essential. • Your contributions should have led to measurable improvements in inference latency, throughput, and memory footprint for domain-specific applications, particularly on resource-constrained devices and edge platforms. • A deep understanding of modern model serving architectures and inference optimization techniques is required. • Strong expertise in writing GPU kernels for mobile devices (i.e., smartphones). • Practical experience in developing and deploying end-to-end inference pipelines, from optimizing models for efficient serving to integrating these solutions on resource-constrained devices is required. • Demonstrated ability to apply empirical research to overcome challenges in model serving, such as latency optimization, computational bottlenecks, and memory constraints. • Proficient in designing robust evaluation frameworks and iterating on optimization strategies to continuously push the boundaries of inference performance and system efficiency. • Distributed Inference Systems: Designing and optimizing high-performance inference engines using techniques like Tensor Parallelism, Pipeline Parallelism, and Expert Parallelism to handle massive models on GPU clusters. • Deep understanding of the math and structure behind Diffusion Models and Vision Transformers.

🏖️ Benefits

• Health insurance • Flexible working hours • Paid time off • Professional development opportunities