Spotify

Personalization

BackendEngineer-Personalization-Tunesday

$152–190k New York, New York, United States Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Mid+ candidates.

The Brief

“Backend Engineer - Personalization - Tunesday at Spotify. Skills: Backend engineering, Personalization, Recommendation systems, Data pipelines. Design services for recommendations. Operate services for recommendations”

What You'll Achieve.

Make deciding what to play easier; Make deciding what to play enjoyable; Keep millions of users listening; Make great recommendations; Find right content at right time; Help users discover new artists; Help users discover new tracks; Improve user's music discovery experience

Industry & Context.

Personalization
Problems you'll solve

Root cause analysis; Troubleshooting

Eligibility Requirements

On-call burden

What They're Looking For.

Must Have

Several years backend engineer experience, Java skills, Comfort with gRPC, Comfort with Protocol Buffers, Hands-on large-scale data pipelines experience, Comfort working across full backend stack, Experience with online serving, Experience with offline data, Experience shipping reliable systems, Experience with SLOs, Experience with on-call burden, Experience with correctness, Experience with experimentation, Experience with fast iteration, Use data to make decisions, Comfort working with data scientists

Nice to Have

Apache Beam/Scio experience, Spark experience, Flink experience, Flyte experience, Bigtable experience, Memcached experience, low-latency APIs experience, BigQuery experience, Dataflow experience, dbt experience, Curiosity about recommendation systems, Curiosity about search, Curiosity about personalization, GCP experience, Kubernetes/GKE experience, Gantry experience, Dataflow experience, BigQuery experience, Bigtable experience, Elasticsearch experience

What You'll Do.

Design services for recommendations

Operate services for recommendations

Serve recommendations in real time

Build batch pipelines

Maintain batch pipelines

Generate candidate pools

Generate bloom filters

Generate personalization signals

Develop components for Sessions Platform

Maintain components for Sessions Platform

Power playlist experiences

Serve recommendations

Collaborate with data scientists

Operationalize research ideas

Expand music discovery definition

Integrate audio attributes

Drive architecture decisions

Set engineering standards

Help team ship faster

How You'll Work.

Team & Collaboration

Multi-functional team; Data scientists; Product managers

Full Job Description

## Description The Personalization team makes deciding what to play next easier and more enjoyable for every listener. From Blend to Discover Weekly, we’re behind some of Spotify’s most-loved features. We built them by understanding the world of music and podcasts better than anyone else. Join us and you’ll keep millions of users listening by making great recommendations to each and every one of them. On the Tunesday squad, our mission is simple! When a user is looking for new music, we help them find the right content at the right time. Every week, hundreds of millions of people discover new artists and tracks through the experiences we build - Discover Weekly, Smart Shuffle, Playlist Extender, and This Is Artist. We are a team of backend & data engineers, data scientists & product experts but most of all, we are passionate about music! As a Backend Engineer on Tunesday, you will own and evolve the systems behind some of Spotify's most-loved playlist experiences. Your work will range from low-latency gRPC services handling real-time recommendation requests, to large-scale daily batch pipelines that process hundreds of millions of user signals to surface the right tracks. You will work closely with data scientists and product managers to bring new recommendation ideas to production and you will help define the engineering bar for how we build and evaluate those experiences.   ## What You'll Do Design and operate services that serve personalized recommendations to users in real time, including Smart Shuffle and Discover Weekly Build and maintain large-scale batch pipelines in Scala/Scio and Flyte that generate candidate pools, bloom filters, and personalization signals for hundreds of millions of users daily Develop and maintain components within Spotify's Sessions Platform (SSP) that power playlist experiences end to end, from candidate retrieval to final serving Collaborate with data scientists to operationalize research ideas, for example, expanding our definition

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