Building Scalable Machine Learning Infrastructure

12 hours ago

Melbourne, Victoria, Australia beBeeMachineLearning Full time $120,000 - $150,000
Machine Learning Engineer

We are seeking an experienced engineer to develop and maintain systems that compute and serve millions of features every day, powering Machine Learning models. The successful candidate will have a strong background in software engineering, with at least 3+ years in large-scale data or ML infrastructure.

  • Key Responsibilities:
  • Design and build scalable systems for feature computation and serving, ensuring high performance, reliability, and scalability.
  • Partner closely with Data Scientists, ML Modelers, and Software Engineers to deliver reliable, performant, and scalable systems.
  • Drive architectural decisions, system reliability improvements, and infrastructure automation.
  • Mentor and guide other engineers on best practices in large-scale distributed systems, feature engineering, and ML infrastructure.

Requirements:

  • 5+ years of experience in software engineering, with at least 3+ in large-scale data or ML infrastructure.
  • Strong proficiency in programming languages such as Java, Python, Kotlin, or Go.
  • Experience building and operating distributed data systems at scale (e.g., Spark, Flink, Kafka, Databricks, Snowflake).
  • Proven ability to design for high performance, scalability, and reliability.
  • Deep knowledge of cloud infrastructure (AWS, GCP) and containerized systems (Kubernetes, Docker).
  • Strong collaboration skills and ability to work cross-functionally with ML practitioners and infrastructure engineers.

Nice to Have:

  • Familiarity with ML workflows, platforms, and systems.
  • Experience building or maintaining ML batch or real-time feature systems.
  • Background in MLOps, experimentation platforms, or ML observability.

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