Staff Engineer, Machine Learning & Data Infrastructure

Improve model inference latency, throughput, resource utilization, and cost. Profile the full prediction path —from feature retrieval and preprocessing to model execution — and…

Build reusable infrastructure for model packaging, versioning, deployment, serving, monitoring, and rollback across cloud platforms and regions.

Design and optimize offline data pipelines on Databricks, Apache Spark, and warehouse/lakehouse technologies, addressing data modeling, partitioning, joins, shuffles, skew, incremental processing, and cost.

Connect offline and online feature pipelines, ensuring point-in-time correctness, consistent feature definitions, freshness, lineage, and reproducible backfills.

A record of building and operating large-scale distributed systems, ML infrastructure, or data platforms, with technical ownership across teams.

Hands-on experience optimizing production model performance, supported by measurable improvements in latency, throughput, utilization, or cost.

Deep understanding of Databricks and Spark, or comparable offline data platforms, including execution plans, distributed query optimization, storage layouts, data correctness…

Experience with the model lifecycle, including training/serving integration, validation, deployment, observability, and rollback.