AI Research Engineer (Multi-Modal & Vision)
Tether
13 days
Remote, Bangalore, Karnātaka, India
•
Engineer
Research
AI
Blockchain
Fintech
Stablecoin
Machine Learning
Remote
Vision
Multi-Modal
Data Curation
Model Optimization
Vision-Language Models
Multi-Modal Architectures
Training Pipeline Design
Model Evaluation
Supervised Fine-tuning
Knowledge Distillation
Reinforcement Learning from Human Feedback
Parameter-Efficient Fine-tuning
Distributed Training Frameworks
Distributed GPU Infrastructure
Compression Techniques
Optimization Techniques
Open-source
GitHub
HuggingFace
NeurIPS
ICML
ICLR
CVPR
ECCV
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 cutting-edge solutions empower businesses—from exchanges and wallets to payment processors and ATMs—to seamlessly integrate reserve-backed tokens across blockchains. By harnessing the power of blockchain technology, Tether enables you to store, send, and receive digital tokens instantly, securely, and globally, all at a fraction of the cost. Transparency is the bedrock of everything we do, ensuring trust in every transaction. Tether Data is fueling breakthroughs in AI and peer-to-peer technology, reducing infrastructure costs and enhancing global communications with cutting-edge solutions like KEET, our flagship app that redefines secure and private data sharing. Our team is a global talent powerhouse, working remotely from every corner of the world. If you’re passionate about making a mark in the fintech space, this is your opportunity to collaborate with some of the brightest minds, pushing boundaries and setting new standards. We’ve grown fast, stayed lean, and secured our place as a leader in the industry. Excellent English communication skills are required. As a member of the AI model team, you will drive innovation in training and optimizing vision-language models with a focus on real-world deployment. Your work will span the full model development lifecycle - from data curation and training pipeline design to model evaluation and optimization - with the goal of building models that are both highly capable and practical to deploy at scale. You will work across a wide spectrum of multimodal architectures integrating text and vision, applying state-of-the-art research to improve model quality, efficiency, and domain-specific performance. This role requires a research-driven mindset combined with strong engineering discipline. Responsibilities include: Conduct end-to-end research and engineering on vision-language models, covering training, evaluation, and optimization. Design and implement post-training pipelines including supervised fine-tuning, knowledge distillation, and reinforcement learning from human feedback. Develop and maintain high-quality multimodal datasets, including data curation, filtering, and balancing for domain-specific tasks. Drive model efficiency and deployability, adapting models for resource-constrained environments using compression and optimization techniques. Design and implement evaluation frameworks and benchmarks to measure model performance, robustness, and real-world task success. Build and scale training workflows across distributed GPU infrastructure. Identify and resolve bottlenecks in training pipelines to achieve state-of-the-art model quality on target benchmarks. Contribute to and leverage open-source ecosystems including models, datasets, and tooling to accelerate development. Stay current with the latest research in multimodal learning and vision-language systems, translating relevant findings into practical improvements. Publish research findings in top-tier AI conferences and journals where applicable.