Match Group

Senior Software Engineer, Machine Learning Infrastructure

Match Group

full-time

Posted on:

Origin:  • 🇺🇸 United States • California

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Salary

💰 $245,000 - $260,000 per year

Job Level

Senior

Tech Stack

CassandraCloudDistributed SystemsHadoopKafkaSpark

About the role

  • Our Mission: Launched in 2012, Tinder revolutionized how people meet, growing from 1 match to one billion matches in just two years. This rapid growth demonstrates its ability to fulfill a fundamental human need: real connection.
  • In 2024, Tinder won four Effie Awards for its first-ever global brand campaign, “It Starts with a Swipe”.
  • Our Values: One Team, One Dream; Own It; Never Stop Learning; Spark Solutions; Embrace Our Differences.
  • The Team: The Engineering team is responsible for building innovative features and resilient systems that bring people together. We're always experimenting with new features to engage with our members. The machine learning infrastructure team develops infrastructures and systems that support end-to-end machine learning development cycle, including training, serving, feature management, model management, monitoring and observability, etc.
  • About The Role: We are looking for a Sr. Software Engineer to help shape and develop the future of Tinder’s Machine Learning infrastructure. Your work will empower teams across Tinder to rapidly test hypotheses and scale their experiments on massive datasets, reaching hundreds of billion data points. In this role, you’ll collaborate closely with multiple machine learning teams to drive impactful innovation.
  • Where you'll work: This is a hybrid role and requires in-office collaboration three days per week. This position is located in Palo Alto, CA

Requirements

  • 5+ years of experience with developing platforms related to machine learning training, serving, feature management, experimentation, etc.
  • Bachelor’s degree in Computer Science, Engineering, Technology, or a related field.
  • Strong understanding of data-driven development, reliability principles, and responsible experimentation practices.
  • Extensive experience with big data processing and storage frameworks such as Hadoop, Spark, Flink, Cassandra, or Kafka.
  • Solid foundation in machine learning concepts like regression, classification, clustering, training, testing, validation, and performance measurement.
  • Proven ability to lead cross-functional initiatives, communicate effectively, and collaborate in team-oriented environments.
  • A strong background in distributed systems, cloud computing, and MLOps, with hands-on experience in working with transformers and other deep learning architectures.
  • Bridge the gap between cutting-edge research and production-ready AI systems.