On-Device ML Engineer: Multimodal Vision & NLP in London

On-Device ML Engineer: Multimodal Vision & NLP in London

London Full-Time 60000 - 80000 Β£ / year (est.) No working from home possible
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At a Glance

  • Tasks: Develop on-device ML models for a child-safety app, optimising detection and classifiers.
  • Company: Jack & Jill, a mission-driven company in London focused on child safety.
  • Benefits: Competitive salary, direct reporting to the founder, and impactful work.
  • Other info: Join a dynamic team and make a real difference in children's lives.
  • Why this job: Own the ML capability and contribute to a product with a genuine social mission.
  • Qualifications: Experience in machine learning and a passion for child safety.

The predicted salary is between 60000 - 80000 Β£ per year.

Jack & Jill in London is seeking a Machine Learning Engineer to develop on-device ML models for a child-safety app. You will optimize YOLO-style detection and transformer classifiers, creating a privacy-first safety layer across applications. This unique role allows you to own the whole ML capability, reporting directly to the founder, while working on a remarkable product that has a genuine social mission supported by high-profile figures.

On-Device ML Engineer: Multimodal Vision & NLP in London employer: Jack & Jill

Jack & Jill offers an exceptional work environment for those passionate about technology and social impact. As an On-Device ML Engineer, you will enjoy a collaborative culture that prioritises innovation and personal growth, with direct access to leadership and the opportunity to contribute to a meaningful product aimed at enhancing child safety. Located in London, the company provides a unique blend of professional development and the chance to make a real difference in the community.

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Contact Details:

Jack & Jill Recruitment Team

We think you need these skills to ace On-Device ML Engineer: Multimodal Vision & NLP in London

Machine Learning
On-Device ML Models
YOLO-style Detection
Transformer Classifiers
Privacy-first Design
Model Optimisation
Computer Vision