Summer Internship, Machine Learning Engineer Intern | Platform Mission (US)
New York City
Applications have closed
Spotify
We grow and develop and make wonderful things happen together every day. It doesn't matter who you are, where you come from, what you look like, or what music you love. Join the band!We are looking for a Machine Learning Engineer Intern to Join The Band and help drive a data-first culture across Spotify. You will work on a variety of problems such as content recommendation, personalization, optimization, user intelligence, and content classification. You will work with a team to come up with new and interesting hypotheses, test them, and scale them up to huge data sets with hundreds of billions of data points. Above all, your work will impact the way the world experiences music.
What you'll do
- Apply machine learning, collaborative filtering, NLP, and deep and reinforcement learning methods to massive data sets.
- Prototype new algorithms, evaluate with small scale experiments, and later productionize solutions at scale to our 140 million active users.
- Collaborate with a cross functional agile team of software engineers, data engineers, ML experts, and others to build new product features.
- Help drive optimization, testing and tooling to improve data quality.
- Iterate on recommendation quality through continuous A/B testing.
Who you are
- You are pursuing a Bachelor’s or Master's degree in Machine Learning, Statistics or related field preferred.
- You have a graduation date of 2024.
- You currently have valid work authorization to work in the country in which this role is based that will extend from June to August 2023.
- You have experience implementing machine learning systems at scale in Java, Scala, Python or similar (not just R or Matlab).
- You care about agile software processes, data-driven development, reliability, and responsible experimentation.
- You preferably have experience with data processing and storage frameworks like Flink, Hadoop, Scalding, Spark, Storm, Cassandra, Kafka, etc.
Where you'll be
- We are a distributed workforce enabling our band members to find a work mode that is best for them!
- Where in the world? For this role, it can be within the Americas region in which we have a work location. Preferably in New York.
- Prefer an office to work from home instead? Not a problem! We have plenty of options for your working preferences. Find more information about our Work From Anywhere options here.
- Working hours? We operate within the Eastern Standard time zone for collaboration.
The United States hourly rate for this position is 33.00 USD (Undergraduate First Year & Sophomores), 42.00 USD (Undergraduate Juniors & Seniors), 49.00 USD (Masters) & 58.00 USD (PhD) per hour plus a one time intern stipend of 2,253 USD. This position is overtime eligible. These rates may be modified in the future.
Spotify is an equal opportunity employer. You are welcome at Spotify for who you are, no matter where you come from, what you look like, or what’s playing in your headphones. Our platform is for everyone, and so is our workplace. The more voices we have represented and amplified in our business, the more we will all thrive, contribute, and be forward-thinking! So bring us your personal experience, your perspectives, and your background. It’s in our differences that we will find the power to keep revolutionizing the way the world listens.
Spotify transformed music listening forever when we launched in 2008. Our mission is to unlock the potential of human creativity by giving a million creative artists the opportunity to live off their art and billions of fans the chance to enjoy and be passionate about these creators. Everything we do is driven by our love for music and podcasting. Today, we are the world’s most popular audio streaming subscription service.
Tags: A/B testing Agile Cassandra Classification Data quality Flink Hadoop Java Kafka Machine Learning Matlab NLP PhD Python R Reinforcement Learning Scala Spark Statistics Streaming Testing
Perks/benefits: Career development Home office stipend
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