• Own and operate GPU and accelerator clusters • Design infrastructure that enables Kraken teams to run models locally • Build and improve scheduling, orchestration, placement, quota management • Optimize inference pipelines for latency, throughput, reliability • Partner with ML engineers to remove bottlenecks in workflows • Build observability for GPU utilization and capacity pressure • Drive reliability, incident response, and post-incident improvements • Evaluate and integrate new hardware and cloud resources • Contribute to long-term architecture decisions

• Own and operate GPU and accelerator clusters • Design infrastructure for local model execution • Build and improve scheduling and orchestration systems • Optimize inference pipelines • Partner with ML engineers to remove bottlenecks

• Own the FHIR R4 data model design • Architect and implement the HealthLake data layer including ingest pipelines • Lead the Supabase-to-HealthLake migration • Design and enforce encryption patterns and PHI access controls • Review and approve KMS infrastructure code • Build the data access layer consumed by agents • Implement audit logging infrastructure that satisfies HIPAA requirements • Design Clean Rooms configurations for analytics use cases

• Architect and scale a high-performance data lakehouse on GCP, leveraging technologies like StarRocks, Apache Iceberg, GCS, BigQuery, Dataproc, and Kafka. • Design, build, and optimize distributed query engines such as Trino, Spark, or Snowflake to support complex analytical workloads. • Implement metadata management in open table formats like Iceberg and data discovery frameworks for governance and observability using Iceberg compatible catalogs. • Develop and orchestrate robust ETL/ELT pipelines using Apache Airflow, Spark, and GCP-native tools (e.g., Dataflow, Composer). • Collaborate across departments, partnering with data scientists, backend engineers, and product managers to design and implement