Informacje o stanowisku

Join Tether and Shape the Future of Digital Finance

At Tether, we’re not just building products, we’re pioneering a global financial revolution. Our solutions empower businesses—from exchanges and wallets to payment processors and ATMs—to seamlessly integrate reserve-backed tokens across blockchains. We enable secure, instant, and global digital token transactions at a fraction of traditional costs, with transparency at our core to ensure trust.

Innovate with Tether

Tether Finance: Features the trusted stablecoin USDT and pioneering digital asset tokenization services.

Tether Power: Focuses on sustainable growth through eco-friendly energy solutions for Bitcoin mining.

Tether Data: Advances in AI and peer-to-peer tech, including KEET, our privacy-focused data sharing app.

Tether Education: Provides digital learning access to empower individuals in the digital economy.

Tether Evolution: Merges technology and human potential to push innovation forward.

Why Join Us?

Our global, remote team offers a unique opportunity to work with top minds in fintech, driving industry standards and innovation. If you excel in English communication and are eager to contribute to our cutting-edge platform, Tether is the place for you.

Are you ready to be part of the future?

About the job:

As part of our AI model team, you will develop evaluation frameworks for pre-training, post-training, and inference, focusing on metrics like accuracy, latency, throughput, and memory. Your work will span resource-efficient models for limited hardware to complex multi-modal architectures.

You should have expertise in model architectures, evaluation practices, and benchmarking. Your role involves designing, testing, and implementing evaluation strategies, collaborating with cross-functional teams, and building performance dashboards to improve AI model reliability and efficiency in real-world applications.

Responsibilities:

• Develop and deploy evaluation frameworks measuring key performance indicators across model stages.
• Create evaluation datasets and benchmarks to ensure consistent, reliable model assessments.
• Collaborate with product, engineering, and data teams to align evaluation metrics with business goals, presenting insights via dashboards and reports.
• Analyze evaluation data to identify bottlenecks, propose, and implement performance improvements.
• Refine evaluation methodologies through experiments and research, staying updated on emerging techniques to enhance model robustness.

Minimum Requirements:

• Degree in Computer Science or related field; PhD preferred in NLP, Machine Learning, with a strong research record.
• Experience designing and evaluating AI models throughout their lifecycle, with proficiency in evaluation frameworks for accuracy, convergence, and robustness.
• Strong programming skills, experience with benchmarking pipelines, and familiarity with metrics like latency, throughput, and memory footprint.
• Proven ability to conduct iterative experiments, stay current with trends, and improve evaluation strategies.
• Experience working with cross-functional teams, translating technical insights into actionable improvements.

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