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Senior Machine Learning Engineer
Content Intelligence
Confirmed live in the last 24 hours
Locations
New York, NY, USA
Experience Level
Entry
Junior
Mid
Senior
Expert
Desired Skills
Agile
Apache Spark
AWS
Data Analysis
Google Cloud Platform
C/C++/C#
Java
Leadership
Management
Research
Scala
SQL
Tensorflow
Natural Language Processing (NLP)
Python
Requirements
  • Extensive experience working in a product and data-driven environment (Python, Scala, Java, SQL, or C++, with Python experience required) and cloud platforms (GCP or AWS)
  • Understand storage solutions and when to use them (e.g. Graph Database, Cassandra, Relational database)
  • Familiarity with Graph ML and graph learning problems & solutions (e.g., graph embedding and graph neural networks)
  • Deep expertise in graph building, graph processing, graph querying, and graph analytics
  • You have experience architecting data pipelines and are self-sufficient in getting the data you need to build and evaluate models, using tools like Dataflow, Apache Beam, or Spark
  • Academic and/or proven experience in knowledge graphs, data management, natural language processing
  • Familiar with the industry trends and keep up with the latest product offerings, and can understand trade-offs of existing solutions
  • Have excellent communication skills and the ability to translate business intuition into data-driven hypotheses that result in impactful engineering solutions
  • Love your customers even more than your code
  • Have experience and passion for mentoring and encouraging collaborative teams
  • Have experience in encouraging a strong engineering culture in an agile environment
Responsibilities
  • Oversee and guide the design, development, and evolution of our knowledge graph ecosystem
  • Coordinate with Product and Engineering leadership to identify both the long-term and short-term needs of the knowledge graph
  • Build and deploy robust ML/DL models that improve entity extraction, classification, resolution, and disambiguation within the Music Knowledge Graph across multiple languages (e.g. English, Korean, etc.), time dimensions, and territories
  • Collaborate with data engineers, applied ML engineers, software engineering, data/content analysts, research scientists & front-end engineers to support tooling for an increasing number of Music Knowledge Graph use cases within Spotify
  • Collaborate with technical and non-technical business partners to develop analytics and metrics that describe the performance of matching systems and the quality of our data
  • As a multi-functional resource, you will have the opportunity to work on the problems where you are needed most, whether that is with an existing project or cutting a path for something new
  • Take on complex data-related problems involving some of the most diverse datasets available and determine the feasibility of projects through quick prototyping with respect to performance, quality, time, and cost using Agile methodologies
  • Architect best-in-class infrastructure (platforms, tools, and approaches) to accelerate our research to the production phase and to unblock efficient deployment, optimization, and testing of ML models
  • Be a leading voice in an active community of machine learning practitioners across Spotify and use existing state-of-the-art tooling in the Spotify ecosystem. (TensorFlow, Kubeflow, DataFlow, python-beam, Google Cloud Platform)
  • Contribute to our team-wide product ideation in collaboration with other engineers, researchers, product managers, and subject-matter experts on the team
  • Your critical projects will involve building enriched canonical versions of the knowledge graph from discrete data sources
Spotify

5,001-10,000 employees

Leading music and podcast streaming platform
Company Overview
Spotify's 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 opportunity to enjoy and be inspired by it. Today, Spotify is the world’s most popular audio streaming subscription service with 406m users, including 180m subscribers, across 184 markets.
Benefits
  • Extensive learning opportunities, through our dedicated team, GreenHouse
  • Global parental leave, six months off - fully paid - for all new parents
  • Flexible public holidays, swap days off according to your values and beliefs
  • Flexible share incentives letting you choose how you share in our success
  • All The Feels, our employee assistance program and self-care hub
  • Spotify On Tour, join your colleagues on trips to industry festivals and events
Company Values
  • Innovative: We move fast and take big risks
  • Sincere: We have no time for internal politics
  • Passionate: We revel in what we do
  • Collaborative: We recognize that we're all in this together
  • Playful: We don't take ourselves too seriously