Artificial Intelligence Researcher

Artificial Intelligence Researcher

Full-Time 63000 - 77000 Β£ / year (est.) Working from home possible
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At a Glance

  • Tasks: Research and develop cutting-edge AI models and tools for creative industries.
  • Company: MITO AI, a leading innovator in artificial intelligence.
  • Benefits: Remote work flexibility, competitive salary, and opportunities for professional growth.
  • Other info: Work remotely from anywhere in Europe and collaborate with top experts.
  • Why this job: Join a pioneering team and shape the future of generative AI technology.
  • Qualifications: PhD in relevant field and experience with AI model development.

The predicted salary is between 63000 - 77000 Β£ per year.

AI Research Engineer (PhD) β€” Generative diffusion models, flow-matching models, or multimodal transformers; multimodal representation learning, cross-modal retrieval, or personalisation in generative AI systems; evaluating image, video, and audio outputs where quality depends on human judgement; fine-tuning, post-training, or training generative, multimodal, reward, or evaluation models; distributed training, model serving, or inference optimisation.

Also useful: experience building AI products or creative tools for filmmakers or designers; Java or TypeScript (our product stack).

Artificial Intelligence Researcher employer: MITO AI

MITO AI is an exceptional employer, offering a unique opportunity to work at the forefront of AI technology in a collaborative and innovative environment. With a strong focus on employee growth, you will have substantial ownership from day one, shaping the future of creative production while enjoying competitive compensation and the flexibility of remote work, particularly from vibrant locations like Madrid. Join us to tackle real-world challenges in AI and make a meaningful impact in the film and media industry.

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Contact Details:

MITO AI Recruitment Team

We think you need these skills to ace Artificial Intelligence Researcher

Generative Diffusion Models
Flow-Matching Models
Multimodal Transformers
Multimodal Representation Learning
Cross-Modal Retrieval
Personalisation in Generative AI Systems
Evaluating Image Outputs