At TuneIn, we are reinventing radio for a connected world, with live sports, up-to-the-minute news, curated music, millions of podcasts, and over 120,000 streaming radio stations—streamed to tens of millions of customers through our mobile and web apps, and our unmatched platform of hundreds of consumer device and service integrations. From smartphone to smart speaker to electric car, TuneIn delivers live and on-demand audio from voices you trust wherever you want to listen.
Location: Remote in Ukraine
About the Team
Our team leverages distributed systems, machine learning, and personalized search to connect listeners to the content they adore, and to derive insights that enable us to deliver flawless user experiences. Help us invent the systems that will power the future of the radio!
About the Role
As a member of our team, you will join us in architecting the future of TuneIn’s data and machine learning platforms. You will leverage technologies such as Apache Airflow, Kafka, and Spark, to build real-time data pipelines capable of processing billions of events across over 200 connected device types. The infrastructure we build will be a core part of TuneIn’s future.
What We Are Looking For
- 5+ years of experience with large-scale data sets
- Expert in engineering data pipelines using big data technologies (Kafka Streaming, Spark Streaming, or Flink)
- Highly proficient in at least one of Java, Python
- Comfortable with complex SQL (Redshift, Snowflake)
- Experience with real-time and streaming data
- Understand the Data Lifecycle and concepts such as lineage, governance, privacy, retention, anonymity, etc
- Conceptually familiar with AWS cloud resources (S3, EC2, RDS, etc)
- Experience with modern deployment and CI/CD practices (Docker, Kubernetes, and Spinnaker)
- Hungry for impact and insatiably curious about new technologies and frameworks
- Ready to build new things without being constrained by technical debt, in a nimble, startup-like environment.
What You Will Do
- Engineer efficient, adaptable, and scalable data pipelines to process structured and unstructured data
- Implement real-time event aggregation pipelines to power content & user insights, quality monitoring, and experimentation
- Maintain and rethink existing datasets and pipelines to service a wider variety of use cases
- Build scalable infrastructure for rapid deployment of machine learning models in production environments
- Design modern personalization & experimentation systems (A/B Testing, Multi-armed bandits).
- Medical Insurance;
- Technology Stipend: covers your work usage of your cell phone and internet;
- Paid Parental Leave: we offer maternity, and paternity leaves to allow employees to bond with their newborns;
- Paid Time Off: 20 working days of vacation, 15 paid sick leave;
- Accounting Support;
- Extended Holiday Office Closures: additional 2 weeks of vacation;
- Learning Support: access to LinkedIn Learning, an annual budget for professional development;
- Wellness Stipend: gym membership or wellness resources of your choosing;
- Coworking stipend.