
11 - 50 employees
Founded 2014
âż Crypto
đł Fintech
đž Finance
Crypto âą Fintech âą 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.
đ„ 12 hours ago
đȘđș Europe â Remote
â° Full Time
đĄ Mid-level
đ Senior
đ§ AI Research Scientist
đ» Ghost score 25%
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11 - 50 employees
Founded 2014
âż Crypto
đł Fintech
đž Finance
Crypto âą Fintech âą 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.
âą Design and deploy state-of-the-art model serving architectures with high throughput, low latency, and optimized memory usage âą Ensure inference pipelines run efficiently across resource-constrained devices and edge platforms âą Establish performance targets for latency, token response, and memory footprint âą Build, run, and monitor controlled inference tests in simulated and live production environments âą Track latency, throughput, memory consumption, and error-rate KPIs âą Document iterative results and compare outcomes against established benchmarks across platforms âą Identify and prepare test datasets and simulation scenarios for low-resource deployment challenges âą Analyze computational efficiency and diagnose serving-pipeline bottlenecks âą Optimize batch processing, network delays, memory usage, scalability, and reliability âą Collaborate with cross-functional teams to integrate optimized serving and inference frameworks into production edge and on-device pipelines âą Define success metrics and perform continuous monitoring and iterative refinements
âą A degree in Computer Science or related field âą Ideally PhD in NLP, Machine Learning, or a related field âą Solid track record in AI R&D, with good publications in A* conferences âą Knowledge of Metal Shading Language (MSL) âą Ability to write custom compute shaders from scratch âą Proven experience in low-level kernel optimizations and inference optimization on mobile devices âą Measurable improvements in inference latency, throughput, and memory footprint for domain-specific applications âą Deep understanding of modern model serving architectures and inference optimization techniques âą Strong expertise in writing GPU kernels for mobile devices such as smartphones âą Deep understanding of model serving frameworks and engines âą Practical experience developing and deploying end-to-end inference pipelines on resource-constrained devices âą Ability to apply empirical research to model-serving challenges including latency optimization, computational bottlenecks, and memory constraints âą Proficiency in designing evaluation frameworks and iterating on optimization strategies âą Experience with distributed inference systems, including Tensor Parallelism, Pipeline Parallelism, and Expert Parallelism âą Deep understanding of the mathematics and structure of Diffusion Models and Vision Transformers âą Understanding of Pruning, Quantization, Flash Attention, KV Cache, and Speculative Decoding (Eagle)
âą Remote work from anywhere in the world âą Opportunity to collaborate with a global team âą Work on innovative fintech, blockchain, AI, and digital finance products
Apply Nowđ September 17
Lead Applied Scientist reverse-engineering generative AI brand recommendations for SE Ranking's SEO platform. Building experiments, models, and measurement products for AI visibility.