Software Engineer, ML Data

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Company Overview

A leading AI-powered performance marketing platform supports the mobile app economy by helping app marketers acquire and retain high-value users while enabling publishers to maximize revenue across programmatic and direct demand. Its technology solutions serve thousands of mobile businesses across more than 70 countries and span industries including gaming, social, finance, ecommerce, and entertainment.

The organization operates a global technology platform focused on machine learning, performance marketing, data intelligence, and scalable infrastructure. Its engineering teams work on large-scale systems that process significant volumes of machine learning inferences and support next-generation AI technologies.

The organization is building a next-generation Machine Learning Platform to support neural networks at significant scale and increase the intelligence and capabilities of its machine learning models.

The organization is seeking a Software Engineer, ML Data to join the Machine Learning Data Platform team and help design and build the infrastructure supporting large-scale machine learning workloads. The role will involve working with experienced ML, software, and infrastructure engineers to develop reliable, scalable, and cost-efficient systems for data lake infrastructure, dataset generation, model training, analytics, reporting, and monitoring.

The successful candidate will have strong software engineering and computer science fundamentals, with particular expertise in Python and Golang. Experience with machine learning platforms, large-scale data processing, Spark, and high-growth technology environments will be valuable.

Key Responsibilities

• Collaborate with experienced Machine Learning, Software, and Infrastructure Engineers to build and evolve the organization’s next-generation ML platform.
• Design, engineer, and implement reliable, scalable, and cost-efficient systems supporting machine learning workloads.
• Build and maintain data lake infrastructure for large-scale machine learning applications.
• Develop systems and tooling for dataset generation and management.
• Support model training infrastructure and enable next-generation machine learning models and technologies.
• Develop analytics, reporting, and monitoring infrastructure for machine learning systems.
• Leverage vendor-based technologies such as AWS and Weights & Biases to build scalable ML infrastructure.
• Utilize open-source technologies including PyTorch, PySpark, Trino, Hive, Iceberg, and ClickHouse.
• Work with internal tooling and infrastructure to support machine learning data workflows.
• Help improve the scalability, reliability, efficiency, and cost-effectiveness of ML data systems.
• Deliver business results by enabling the development and deployment of increasingly capable machine learning models.
• Balance engineering quality and technical excellence with the practical need to deliver solutions efficiently.

Required Qualifications

• 6+ years of professional industry experience.
• Very strong programming skills in Python and Golang.
• Strong computer science fundamentals, including data structures, algorithms, and system architecture.
• Experience designing and building reliable and scalable software systems.
• Strong understanding of distributed systems and large-scale infrastructure.
• Ability to design and implement systems that prioritize reliability, scalability, and cost efficiency.
• Strong problem-solving and software engineering skills.
• Passion for quality and technical excellence while maintaining a practical approach to delivering projects.

Preferred Qualifications

• Experience working with Apache Spark or similar large-scale data processing technologies.
• Experience in a high-growth startup or rapidly evolving technology environment.
• Experience building machine learning tooling, platforms, or infrastructure.
• Experience applying machine learning infrastructure to large-scale problems.
• Familiarity with technologies such as AWS, Weights & Biases, PyTorch, PySpark, Trino, Hive, Iceberg, and ClickHouse.
• Experience working with data lakes, dataset generation, model training infrastructure, analytics, reporting, or monitoring systems.

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