Job Location: New York, NY
Engineering
Machine Learning
At Spotify Advertising, our part of that mission is to build the next generation advertising platform for audio which can scale the freemium experience for hundreds of millions of fans and tens of thousands of advertisers. This scale brings unique challenges as well as tremendous opportunities to define the insights product for our business.
Location
New York or Remote Americas
Job type
Permanent
We are looking for a Senior Machine Learning Engineer to join the Podcast Brand Safety product area. As a part of our team, you will be involved in the research and development of ML models in the podcast content understanding space. By joining the Brand Safety ML team at an early stage in product development, you will have high impact potential and be instrumental in the design and implementation of Spotify’s brand safety solution.
What you’ll do
- Research and develop state-of-the-art machine learning and deep learning techniques in the podcast content understanding space to enable brand safe ad placement
- Keep apprised of cutting edge developments in ML literature
- Prototype new approaches and develop solutions that scale
- Set and evangelize standards for rigorous experimentation practices
- Thoughtfully communicate your work and experience through both internal collaborations and mentorship opportunities
- Collaborate with a cross functional agile team spanning design, data science, product management, and engineering to build new technologies and features.
Who you are
- You have deep domain expertise in NLU related problem areas and/or representation learning (experience with Knowledge Graphs is a plus)
- You have hands-on experience implementing machine learning systems at scale in Python
- You have a strong background in machine learning foundations, architectures and software development with a focus on deep learning algorithms
- You care about agile software processes, data-driven development, reliability, and disciplined experimentation
- You have experience and passion for fostering collaborative teams.
- Experience with pyTorch, TensorFlow, and/or Google Cloud Platform is a plus.
- Experience with building data pipelines and getting the data you need to build and evaluate your models, using tools like Apache Beam / Spark is a plus.
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
- 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.
Our global benefits
Extensive learning opportunities, through our dedicated team, GreenHouse.
Flexible share incentives letting you choose how you share in our success.
Global parental leave, six months off – fully paid – for all new parents.
All The Feels, our employee assistance program and self-care hub.
Flexible public holidays, swap days off according to your values and beliefs.
Spotify On Tour, join your colleagues on trips to industry festivals and events.
Learn about life at Spotify
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.
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