Senior Machine Learning Engineer- Afterpay London Full-time

Full time @Data Science Career in Machine Learning Email Job

Job Detail

  • Job ID 23610
  • Sector  Machine Learning

Job Description

Machine Learning

Full job description

Company Description

TIDAL was founded for artists by artists as the next innovative streaming platform to bring value back to the music industry. We empower artists with the products, resources, services, and content required to take control of their careers and connect more deeply with fans. Available in over 60+ countries, TIDAL continues to help artists break down economic barriers to continue creating what’s next in culture. TIDAL is part of Block, Inc. (NYSE: SQ), a global technology company with a focus on financial services.

Job Description

The Personalisation team strives to create a personalised user experience on TIDAL by creating algorithmic playlists, tailoring users homepage recommendations and other ML powered features. Our team is growing and we are looking for ML Engineers who are excited about solving interesting music recommendation problems as part of a smaller team.

Our ML Engineers work in close collaboration with Product, Data Analysts, Design and Product from across TIDAL. You will report to the Personalisation Engineering Manager. In the team we use a wide range of models including simple heuristics, embeddings and deep learning to build our recommender systems. We’re open to in-office, hybrid or fully remote for this role – you choose whatever works the best for you.

You Will:

  • Develop new recommender systems that powers TIDAL’s homepage and algorithmic playlists
  • Build production systems that personalize our listener’s experience on the platform
  • Be a technical leader and establish quality practices that stick, make broader design decisions and set an example for others to follow
  • Collaborate with a cross functional team of designers, product managers and software engineers to build new technologies and features
  • Design experiments, test them on production users, analyze and repeat


You have:

  • 8+ years building and operating quality software
  • 5+ years of experience with recommender systems, ranking systems, or similar
  • Led the development of complex models trained on large datasets powering customer facing features
  • Strong software engineering skills
  • Strong communication skills and customer empathy
  • Experience with PyTorch, PySpark, Databricks and AWS is a plus

Additional Information

We’re working to build a more inclusive economy where our customers have equal access to opportunity, and we strive to live by these same values in building our workplace. Block is a proud equal opportunity employer. We work hard to evaluate all employees and job applicants consistently, based solely on the core competencies required of the role at hand, and without regard to any legally protected class.

We believe in being fair, and are committed to an inclusive interview experience, including providing reasonable accommodations to disabled applicants throughout the recruitment process. We encourage applicants to share any needed accommodations with their recruiter, who will treat these requests as confidentially as possible. Want to learn more about what we’re doing to build a workplace that is fair and square?

Block, Inc. (NYSE: SQ) is a global technology company with a focus on financial services. Made up of Square, Cash App, Spiral, TIDAL, and TBD, we build tools to help more people access the economy. Square helps sellers run and grow their businesses with its integrated ecosystem of commerce solutions, business software, and banking services. With Cash App, anyone can easily send, spend, or invest their money in stocks or Bitcoin. Spiral (formerly Square Crypto) builds and funds free, open-source Bitcoin projects. Artists use TIDAL to help them succeed as entrepreneurs and connect more deeply with fans. TBD is building an open developer platform to make it easier to access Bitcoin and other blockchain technologies without having to go through an institution.

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