AI Research Engineer (Multi-Modal & Vision)

Tether.io

Abu Dhabi, UAE

Full Time

Senior

4 weeks ago

Vision Language ModelsSupervised Fine TuningKnowledge DistillationReinforcement Learning from Human FeedbackParameter Efficient Fine TuningDistributed Training

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Key skills for this role

Vision Language ModelsSupervised Fine TuningKnowledge Distillation

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About the Role

Tether is seeking an AI Research Engineer to drive innovation in training and optimizing vision-language models for real-world deployment. The role spans the full model development lifecycle, from data curation to evaluation, and requires strong experience with multimodal post-training workflows.

Key Skills for This Role

Vision Language ModelsSupervised Fine TuningKnowledge DistillationReinforcement Learning from Human FeedbackParameter Efficient Fine TuningDistributed Training

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
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

Requirements

Degree in Computer Science, Machine Learning, or related field; MS/PhD 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
Proven open source contributions in multimodal AI on GitHub or HuggingFace
Publications at top AI conferences (NeurIPS, ICML, ICLR, CVPR, ECCV etc.)

Full Job Posting

About The Job

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.
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.
You will work closely with a small, high caliber team where your contributions will have direct and meaningful impact.

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.
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.

Qualifications

Degree in Computer Science, Machine Learning, or a related field; MS/PhD 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.
Proven open source contributions in multimodal AI on GitHub or HuggingFace.
Publications at top AI conferences (NeurIPS, ICML, ICLR, CVPR, ECCV etc.)

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