What will your job look like: Design and optimize algorithms and pipelines for large scale model inference Build scalable systems for high throughput data processing and streaming Develop data transformation and preprocessing components for ML workloads Improve performance, efficiency, and reliability across distributed inference systems Work closely with ML researchers, infrastructure, and platform teams Drive architectural decisions for production ML and data systems All you need is: 5+ years of experience in Algorithm Engineering, ML Infrastructure, or Data Systems Strong programming skills in Python Hands on experience with Spark, Polars, Pandas, DuckDB, and AWS Strong understanding of distributed systems, scalability, and performance optimization Experience building or supporting ML inference pipelines in production Strong system design and architecture skills
