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Generative AI Group Leader

AKA Foods is using the power of Deep Learning and Graph Neural Networks in order to digitize the plant-based food tech, map the molecular structure of food and create products with authentic taste and texture. By applying the latest AI methods to food development we are accelerating innovation and predicting unique combinations of ingredients, allowing for mainstream adoption of plant-based alternatives.

Are you someone who loves to lead and create new things?

We're searching for a Generative AI Group Leader to join us. If you're great at both leading a team and doing technical research and problem solving, this is the perfect role for you. You'll be at the forefront of our data science team, helping us develop innovative new generative AI algorithms. Plus, you'll be leading the way in building a collaborative team culture to reach new innovative goals.

Key Responsibilities:

  • Set a vision for generative AI research and development that aligns with company objectives
  • Lead cutting-edge generative AI research initiatives
  • Grow, mentor and inspire the data science team, while attracting and nurturing talents
  • Manage and collaborate on AI projects from conception to deployment, emphasizing teamwork in research and development
  • Oversee the research aspects of deep learning generative AI model development, ensuring excellence in algorithmic problem solving


  • Master's degree in Computer Science, Statistics, Mathematics, or related field, or equivalent professional experience.
  • At least 5 years of experience in Deep learning AI, with a strong foundation in research.
  • Minimum of 3 years of leadership experience of ML/Data Science team.
  • Proficiency in data structures, data modeling.
  • Extensive knowledge of mathematics, statistics, probability theory, and algorithms.
  • Exceptional leadership skills, with a track record of developing high-performing teams.
  • Strong communication skills, able to effectively convey complex AI concepts to a variety of audiences.
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