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Staff/Senior Software Engineer, Machine Learning Platform (Ad Cloud)

Staff/Senior ML Platform Engineer to build scalable ML platform.
Appier
On-Site
Taiwan
Undisclosed salary
Job Description
Appier is seeking a Staff/Senior Machine Learning Platform Engineer to join their Machine Learning Platform Team in Taipei, Taiwan. This role involves shaping the architecture and core components of Appier's ML platform, which supports the daily execution of hundreds of ML models and processes billions of data records across batch and streaming pipelines. The engineer will ensure the platform is robust, scalable, and developer-friendly, while also championing best practices and modern development tools.
  • Architecting and scaling batch (Spark) and streaming (Flink) pipelines.
  • Designing and operating robust ML job execution frameworks.
  • Building and maintaining internal API servers and developer tools on Kubernetes.
  • Designing and monitoring data infrastructure using ClickHouse and PostgreSQL.
  • Ensuring high availability and observability through monitoring tools.
  • Collaborating with data scientists, product managers, and engineers.
  • Adopting and promoting the use of LLM-based tools.
  • Mentoring junior engineers and evolving team engineering culture.
  • Bachelor’s degree in Computer Science, Engineering, or a related field.
  • 4+ years of experience in data systems, machine learning infrastructure, or platform engineering.
  • Strong coding proficiency in Python and/or Java.
  • Experience with Spark, Flink, Kubernetes (GKE), and infrastructure-as-code tools.
  • Experience managing high-throughput data infrastructure using ClickHouse, PostgreSQL, or similar systems.
  • Deep understanding of ML pipelines and distributed job execution.
  • Proven ability to apply LLM-based tools.
  • Strong ownership, architectural thinking, and ability to lead cross-functional platform projects.
  • Opportunity to shape the architecture of a critical ML platform.
  • Exposure to cutting-edge technologies like Spark, Flink, and Kubernetes.
  • Collaborative environment with data scientists, product managers, and engineers.
  • Encouragement to use and promote LLM-based tools for increased productivity.
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