London On-Site ML Engineer β€” Equity & Production ML

London On-Site ML Engineer β€” Equity & Production ML

Full-Time 63000 - 77000 Β£ / year (est.) No working from home possible
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At a Glance

  • Tasks: Build and deploy machine learning models while shaping data pipelines.
  • Company: Few&Far, a dynamic company at the forefront of ML innovation.
  • Benefits: Competitive salary, collaborative environment, and hands-on experience.
  • Other info: Exciting opportunities for growth in a vibrant London setting.
  • Why this job: Make a real impact in a fast-paced network with cutting-edge technology.
  • Qualifications: Experience in machine learning and strong collaboration skills.

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

Few&Far is seeking a Machine Learning Engineer for an in-person role in London.

You will work with data scientists, engineers, and product teams to build models, shape data pipelines, and power decision making across a large, fast-moving network.

Youll design data architecture, deploy production ML, and develop ETL pipelines from diverse sources while collaborating with stakeholders to ensure data is used effectively.

The role emphasizes hands-on development and practical impact.

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London On-Site ML Engineer β€” Equity & Production ML employer: Few&Far

Few&Far is an exceptional employer that fosters a collaborative and innovative work culture, where you will have the opportunity to work alongside talented data scientists and engineers in the vibrant city of London. With a strong emphasis on hands-on development, you will not only contribute to impactful projects but also benefit from continuous learning and growth opportunities within a fast-paced environment. The company's commitment to leveraging cutting-edge technology ensures that your work will be both meaningful and rewarding.

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Contact Details:

Few&Far Recruitment Team

We think you need these skills to ace London On-Site ML Engineer β€” Equity & Production ML

Machine Learning
Data Architecture Design
ETL Pipeline Development
Collaboration with Stakeholders
Model Building
Data Pipeline Shaping
Production ML Deployment