AI Research Engineer (Multi-Modal & Vision)
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Key skills for this role
About the Role
Tether Operations Limited seeks an AI Research Engineer to drive innovation in training and optimizing vision-language models for real-world deployment. You will work across multimodal architectures, from data curation to model evaluation, with a focus on production scalability.
Key Skills for This Role
Responsibilities
- Conduct end to end research and engineering on vision language models
- Design and implement post training pipelines including SFT, knowledge distillation, and RLHF
- Develop and maintain high quality multimodal datasets
- Drive model efficiency and deployability using compression and optimization techniques
- Design and implement evaluation frameworks and benchmarks
- Build and scale training workflows across distributed GPU infrastructure
- Identify and resolve bottlenecks in training pipelines
- Contribute to and leverage open source ecosystems
- Stay current with latest research in multimodal learning and vision language systems
- Publish research findings in top tier AI conferences and journals where applicable
Requirements
- Research driven mindset with strong engineering discipline
- Experience with vision language models and multimodal architectures
- Ability to implement and measure impact of techniques
Full Job Posting
Role Overview
- 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.
- We expect you to bring a research driven mindset combined with strong engineering discipline.
- 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.
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