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High-Performance ML Expert

2 weeks ago

Melbourne, Victoria, Australia beBeeMACHINELEARNING Full time $180,000 - $240,000

About our company

We are a team of innovators that believe in empowering people. Our mission is to enable individuals to achieve their goals through technology.

The Role

We are seeking an experienced Machine Learning Engineer to join our Machine Learning Features team. This team is responsible for developing and maintaining systems that compute and serve millions of ML features daily, powering ML models across various platforms.

Your Responsibilities

  • Scale high-demand ML feature pipelines and services to meet the needs of teams across different departments.
  • Contribute to the design and development of a groundbreaking new ML feature system, redefining how ML features are computed and delivered at scale.
  • Partner 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.
  • Champion engineering excellence through code reviews, technical documentation, and continuous improvement.

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.
  • Experience mentoring junior engineers and driving technical direction on complex projects.

Bonus Points

  • 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.