AI Research Engineer (Multi-Modal & Vision)

Tether Operations Limited

3d•Remote

About The Position

As a member of Tether’s AI model team, the AI Research Engineer will drive research and engineering for training and optimizing vision-language models, with a focus on real-world deployment. The role spans the full model development lifecycle, including data curation, training pipeline design, model evaluation, and optimization, to build capable models that are practical to deploy at scale. The engineer will work across multimodal architectures integrating text and vision, applying current research to improve model quality, efficiency, and domain-specific performance. The role calls for a research-driven approach and strong engineering discipline: selecting suitable techniques, implementing them rigorously, and measuring their impact. The position works closely with a small, high-caliber team.

Requirements

Degree in Computer Science, Machine Learning, or a related field; an MS or PhD is preferred.
Strong experience with multimodal post-training workflows, including supervised fine-tuning, knowledge distillation, and reinforcement learning from feedback.
Hands-on experience with parameter-efficient fine-tuning and distributed training frameworks.
Demonstrated ability to build and improve vision-language models with measurable results on standard benchmarks or real-world tasks.
Experience adapting models for resource-constrained environments.

Nice To Haves

Proven open-source contributions in multimodal AI on GitHub or HuggingFace.
Publications at top AI conferences such as NeurIPS, ICML, ICLR, CVPR, or ECCV.
An MS or PhD degree.

Responsibilities

Conduct end-to-end research and engineering on vision-language models, covering training, evaluation, and optimization across the full model development lifecycle.
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.
Improve model efficiency and deployability 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 training-pipeline bottlenecks to achieve state-of-the-art model quality on target benchmarks.
Contribute to and leverage open-source models, datasets, and tooling to accelerate development.
Stay current with multimodal learning and vision-language research and translate relevant findings into practical improvements.
Publish research findings in top-tier AI conferences and journals where applicable.