Software Engineer

Where we work Udemy is a global company headquartered in San Francisco, with additional U.S. offices in Denver and Austin, and international hubs in Australia, India, Ireland, Mexico, and Türkiye. This is an in-office position, requiring three days a week in the office (Tuesday, Wednesday, Thursday) and flexibility on Mondays and Fridays.About your skillsData & ML Infrastructure: You have experience building data pipelines and/or ML workflows in Python, and are comfortable working with structured data, orchestration tools, and scalable processing frameworks.Cloud Platform Management: You work with cloud platforms (AWS, Azure, or GCP) to deploy and manage infrastructure that supports batch and real-time data processing, as well as experimentation with generative AI models.System Architecture: You contribute to building well-structured, modular systems that support data quality, traceability, and reusability across analytics and AI/ML applications.Cross-team Collaboration: You work effectively with data scientists, ML engineers, and product teams to deliver foundational data capabilities that power AI and product features.About this role As a Data Intelligence Platform Engineer at Udemy, you’ll help build and maintain the core data systems that power our analytics and AI/ML features and applications.Play a key role in advancing Udemy’s mission to democratize education by providing reliable, scalable data infrastructure that supports personalized learning and AI-powered tools.What you’ll be doing Develop and maintain data pipelines and platform components in Python using Spark and orchestration tools to enable reliable data delivery.Collaborate with teams to support use cases across analytics and AI/ML applications.Evaluate and adopt new frameworks and tooling that enhance the platform’s support for AI/ML workloadsBuild and maintain data quality guardrails including validation, lineage tracking, and observability.Learn from and support more senior engineers in shaping the platform’s direction, and grow your skills across data and ML engineering domains.Contribute to privacy- and security-conscious data practices, especially as they relate to AI/ML input/output flows.Participate in on-call rotation during business hours to ensure platform reliability.​What you’ll have Proficiency in Python, data structures, algorithms and design patternsExperience with Databricks and AWS including storage, virtual machines, access management, networking, etcExperience with data processing libraries, (e.g., PySpark, Pandas, Scalable Data Pipelines) data formats (Delta/Iceberg) and/or ML toolkits.Interest or experience working with AI/ML integration frameworks and building workflows involving reasoning, tool usage, or multi-step agents.Strong problem-solving skills and attention to data correctness, latency, and platform performance.Ability to work collaboratively on cross-functional teams and communicate technical ideas clearly.Exposure to streaming data systems such as Apache Kafka or Kinesis.Familiarity with Docker and Kubernetes, and an interest in deploying and scaling AI-related services.

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