Member of Technical Staff

2 months ago

Responsibilities

Own a core platform area end to end, including design, implementation, operations, and long-term technical direction.
Build the cross-portfolio data platform, including the Iceberg data lake, query layer, tenant isolation, ingestion pipelines, canonical data model, catalog, and semantic layer.
Design and operate backend services, identity and access control, audit systems, workflow orchestration, and internal APIs and SDKs.
Build multi-tenant data, compute, and credential boundaries, including cross-cloud AWS and Azure connectivity and regional data residency.
Develop the workflow and action runtime with typed actions, idempotency, retries, rollback paths, human approval gates, and audit trails.
Create observability and evaluation infrastructure including traces, metrics, structured logs, replay systems, regression suites, and safe A/B testing.
Design safety controls such as autonomy tiers, kill switches, per-action blast-radius caps, and audit surfaces.
Contribute to ML- and agent-driven systems while ensuring the underlying platform remains reliable and maintainable.

Staff- or principal-equivalent engineering depth and experience building and operating systems relied on by real businesses.
Clean, idiomatic coding ability in at least one of Python, Go, Rust, or TypeScript, with the ability to work across these languages.
Strong distributed systems experience, including idempotency, partial failure, retry semantics, eventual consistency, schema evolution, and multi-tenant isolation.
Experience building or operating substantial modern data infrastructure using technologies such as Kafka, Spark, dbt, Iceberg, Snowflake, Databricks, or BigQuery.
Platform mindset with strong API design, documentation, migration planning, and developer experience practices.
Ability to make architectural decisions amid ambiguity and communicate technical choices clearly.
Interest in modern ML and the ability to build infrastructure around models; ML research expertise is not required.
Production experience with LLM-driven systems, experience with Iceberg, Polaris, Snowflake, or Databricks at scale, multi-tenant SaaS or platform infrastructure experience, offline RL, contextual bandits, sequential decision-making, or open-source contributions are bonus qualifications.