Apply on Employer Site

Tether.io · 3 months ago

Senior Research Engineer - Multimodal & Video Foundation Model (100% Remote)

United States

Full-time

Remote

Senior Level

Tether.io is pioneering a global financial revolution with its cutting-edge solutions that empower businesses in the digital finance space. We are seeking a Senior Research Engineer to drive innovation in architecture development for multimodal and video generation models, enhancing intelligence and efficiency in AI systems.

BitcoinBlockchainCryptocurrencyDecentralized Finance (DeFi)FinanceFinancial ServicesFinTechSoftware

Responsibilities

Pioneer multimodal and video-centric research that moves fast and breaks ground, contributing directly to usable prototypes and scalable systems

Design and implement novel AI architectures for multimodal language models, integrating text, visual, and audio modalities

Engineer scalable training and inference pipelines optimized for large-scale multimodal datasets and distributed GPU systems across thousands of GPUs

Optimize systems and algorithms for efficient data processing, model execution, and pipeline throughput

Build modular tools for preprocessing, analyzing, and managing multimodal data assets (e.g., images, video, text)

Collaborate cross-functionally with research and engineering teams to translate cutting-edge model innovations into production-grade solutions

Prototype generative AI applications showcasing new capabilities of multimodal foundation models in real-world products

Develop benchmarking tools to rigorously evaluate model performance across diverse multimodal tasks

Qualification

PythonPytorchVideo generation modelsMultimodal researchLarge-scale datasetsComputer VisionMachine LearningNLPApplied Statistics

Required

Bachelor's degree in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience

Expertise in Python & Pytorch, including practical experience working with the full development pipeline from data processing & data loading to training, inference, and optimization

Experience working with large-scale text data, or (bonus) interleaved data spanning audio, video, image, and/or text

Direct hands-on experience in developing or benchmarking at least one of the following topics: LLMs, Vision Language Models, Audio Language Models, generative video models