Senior ML Engineer β€” Personalization & Recommenders (Remote)

Senior ML Engineer β€” Personalization & Recommenders (Remote)

Full-Time 60000 - 80000 Β£ / year (est.) No working from home possible
G

At a Glance

  • Tasks: Lead the design and deployment of real-time recommendation models.
  • Company: Grafana Labs, a remote-first company with a strong culture of openness.
  • Benefits: Competitive salary in GBP, flexible remote work, and a focus on personal impact.
  • Other info: Opportunity to work in a dynamic environment with significant career growth.
  • Why this job: Join a team driving innovation in machine learning and personalization.
  • Qualifications: Experience in machine learning and collaboration with cross-functional teams.

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

Grafana Labs in the United Kingdom (remote) is seeking a Senior Machine Learning Engineer to lead the evolution of an Interactive Learning system. You will design, build, deploy and operate real-time recommendation models and collaborate with engineers, data analysts, and product teams to drive measurable improvements.

This fully remote role focuses on personalization, experimentation, and scalable ML infrastructure, with compensation in GBP and a strong culture of openness, autonomy, and impact.

Senior ML Engineer β€” Personalization & Recommenders (Remote) employer: Grafana

Grafana Labs is an exceptional employer, offering a dynamic remote work environment that fosters collaboration and innovation. With a strong emphasis on employee growth, the company provides RSU incentives and opportunities to engage in meaningful technical pre-sales activities, ensuring that team members can thrive while helping customers succeed with cutting-edge solutions. The culture of openness and support makes Grafana Labs a rewarding place to build a career in the tech industry.

G

Contact Details:

Grafana Recruitment Team

We think you need these skills to ace Senior ML Engineer β€” Personalization & Recommenders (Remote)

Machine Learning
Real-time Recommendation Models
Personalisation Techniques
Scalable ML Infrastructure
Collaboration with Engineers
Data Analysis
Experimentation