I’m an ML systems engineer in Stockholm, working where machine learning meets platform engineering.
I care about the parts around the model: keeping accelerators busy, moving data without losing provenance, making workloads portable across runtimes, and building identity and evaluation boundaries that hold up outside a notebook.
Most of my recent work lives across Python, Spark, Kubernetes, AWS, Databricks, MLflow, and the open-source orchestration ecosystem. I like small experiments that make a systems decision measurable—and larger tools that make the right path easier for the next engineer.
Away from infrastructure, I still enjoy computer vision, robotics, and building products people can actually use.