Senior Manager - Recommendations

SoundCloud Ltd
London
3 months ago
Applications closed

Related Jobs

View all jobs

Senior Manager (Data Science)

Program Manager

Performance Officer / Data Analyst

Senior Product Manager - AI, ML & Data Science

Senior Machine Learning Product Manager (Deploy)

Senior Data Engineering Manager

SoundCloud empowers artists and fans to connect and share through music. Founded in 2007, SoundCloud is an artist-first platform empowering artists to build and grow their careers by providing them with the most progressive tools, services, and resources. With over 400+ million tracks from 40+ million artists, the future of music is SoundCloud.

SoundCloud is the #1 destination to discover what's next in music. And that means, our Music Recommendation is at the core of SoundCloud’s strategy . As the Senior Manager of the Recommendation teams, you will drive technical strategy and execution of projects to help music fans find great music and help creators to find the best possible audience for their music. You will work closely with Product Managers and UX Researchers to develop experiences helping our customers to find great music in our ever-growing catalog of ~300 million tracks. You will manage the Recommendation teams that research, design, develop and maintain recommendation services at SoundCloud. You will be responsible for products such as personalized home feed ranking, personalized playlists generation, related music recommendations and artist stations as well as ideation and delivery of new services and capabilities.

In your role you will have end-to-end ownership of the software engineering and machine learning infrastructure necessary to leverage the data and other resources in order to build and deliver search and recommendation mechanisms. You will prioritize activities, delegate and supervise all engineers’ activities and deliverables. As a people manager you will be responsible for hiring, managing and developing a group of talented software engineers working with cutting-edge technologies such as microservices, Scala, TypeScript/React, Python, data processing platforms such as BigQuery and other Cloud Platform services.You will also grow and work with the team of machine learning engineers and scientists working with variety of ML frameworks including (but not limited to) PyTorch and Tensorflow. We care much more about your general engineering and ML skills and positive attitude towards getting things done than any prior knowledge of a particular language or framework.

About the team: We’re a team of 15 people from over 10 countries. We are passionate about what we do, the problems we’re solving for creators and fans, and of course, music. We value trust between team members, experimentation, accountability, and work/life balance. We are an agile, hard-working team who delivers simply and quickly, while having fun in the process!

Key responsibilities:

Work closely with the Product Managers, UX Researchers and Designers to transform Discovery at SoundCloud. Broadcast the Discovery vision to inspire technical and non-technical stakeholders. Drive short and long-term objectives and key results with full responsibility for executing the OKRs. Be a pragmatic voice showing where to invest engineering effort to drive business outcomes. Lead, support and mentor Engineers and Scientists. Recruit and retain top-notch talents for your teams. Act as a technical liaison and representative for other teams, groups, and the company. Keep in touch with industry trends and technological advances and highlight opportunities that apply to SoundCloud.

Required skills and experience:

Experience with managing engineers and scientists. A strong engineering and data science background, knowledge and experience on recommendation engine and machine learning systems, being able to evaluate complex technical decisions. Industry experience building data-driven products and implementing machine-learning solutions at scale. Experience in maintaining large cloud systems over time, establishing a technical roadmap and balancing product and engineering concerns Extensive experience with and in-depth knowledge of agile methodologies. Demonstrated expertise in problem solving and technical innovation. Recruiting and culturally-informed communication skills.

If you are excited by the opportunity to directly impact the daily experience and happiness of millions of people around the world, giving them the power to share and connect through music, we’d love to hear from you. 

Read about engineering at SoundCloud.

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.