Block is looking for a Senior Machine Learning Engineer to join the Machine Learning Features team within Block's Machine Learning Platform. This team is responsible for developing and maintaining the systems that compute and serve millions of ML features every day, powering Machine Learning models across Block. The role will involve scaling high-demand ML feature pipelines and services, and contributing to the design and development of a new ML feature system to redefine how ML features are computed and delivered at scale.
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)
- Familiarity with ML workflows, platforms, and systems
- Experience building or maintaining ML batch or real-time feature systems
Responsibilities
- Own and maintain mission-critical ML feature computation and serving systems that support millions of daily feature requests
- Design and build the next-generation ML feature platform to enable faster iteration and higher-quality ML models across Block
- 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
- Champion engineering excellence through code reviews, technical documentation, and continuous improvement
Other
- 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
- Background in MLOps, experimentation platforms, or ML observability
