Artificial Intelligence Researcher Engineer

Artificial Intelligence Researcher Engineer

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

  • Tasks: Dive into cutting-edge AI research and develop innovative generative models.
  • Company: Join MITO AI, a leader in AI technology with a remote-first culture.
  • Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
  • Other info: Collaborative environment with a focus on innovation and creativity.
  • Why this job: Shape the future of AI while working on exciting projects that impact creativity.
  • Qualifications: PhD in relevant field and experience with AI models and tools.

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 Engineer employer: MITO AI

At MITO AI, we pride ourselves on being an exceptional employer, offering a dynamic and innovative work culture that fosters creativity and collaboration. Our remote-first approach allows you to work from anywhere in Europe while providing ample opportunities for professional growth and development in the cutting-edge field of AI research. Join us to be part of a team that values your contributions and supports your journey in shaping the future of generative AI systems.

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Contact Details:

MITO AI Recruitment Team

We think you need these skills to ace Artificial Intelligence Researcher Engineer

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