Sr. ML Ops Engineer

Senior ML Ops Engineer role focused on building data infrastructure, training pipelines, and internal tooling for the ML team. Responsibilities include building and maintaining data pipeline infrastructure that consolidates internal infra, labeling tools, S3, and other data sources into a unified, queryable system; developing tooling for dataset selection and curation; owning ML data infra end-to-end from robot data to training runs; building model evaluation and regression testing infrastructure; and automating the model retuning loop so ML engineers can focus on higher-value work. The role involves working in a hybrid or remote setting with periodic trips to HQ for Mountain View, CA. Must-haves include 2-3 years shipping production ML infrastructure, experience building distributed data pipelines, understanding data flow to trained models, and experience building systems from scratch or contributing heavily to infra builds. Nice-to-haves include experience setting up annotation tooling and workflows, а robotics autonomy and computer vision background, and experience integrating with Kubeflow, SLURM, or similar tools for scalable training workflows.