Member of Technical Staff

Beacon Software•New York, NY

15h

About The Position

Members of Technical Staff (MTS) are the senior engineers who build the platform that everything else at Beacon runs on. You will own a piece of the core stack end-to-end: design, implementation, operations, and the long-term technical direction of that area. This is a Staff Engineer role in everything but name. We run flat. The work is systems engineering at its core. Multi-tenant data infrastructure across very different portcos. Event-driven pipelines that have to be correct under partial failure. Service architectures that have to stay simple as the product surface grows. APIs and SDKs that other engineers — including FDEs out in the field — will build on every day. ML and agentic systems are part of the stack. They sit on top of a foundation that has to be solid first. This is not infrastructure for its own sake. The platform has to be solid before anything else at Beacon works. That is the job.

Requirements

Senior engineering depth (Staff or principal-equivalent).
Experience building and operating systems that real businesses depend on.
Proficiency in at least one of Python, Go, Rust, or TypeScript, and ability to work in any of them.
Experience with distributed systems, including production incidents related to idempotency, partial failure, retry semantics, eventual consistency, schema evolution, and multi-tenant isolation.
Experience building or operating systems on a modern data stack (e.g., Kafka, Spark, dbt, Iceberg, Snowflake, Databricks, BigQuery).
Understanding of the difference between a warehouse and a lake, and when each is appropriate.
Platform mindset: building for other engineers, prioritizing developer experience.
Comfort with ambiguity and making decisions with incomplete information.
Interest in modern ML, with the ability to understand and build infrastructure around models.

Nice To Haves

Prior Staff or Principal Engineer experience at a high-bar engineering organization.
Experience with Iceberg, Polaris, Snowflake, or Databricks at scale.
Multi-tenant SaaS or platform infrastructure background.
Production experience with LLM-driven systems, including evals and observability.
Background in offline RL, contextual bandits, or sequential decision-making.
Open-source contributions to data infrastructure, observability, or developer tooling projects.
Shipped LLM-driven systems in production.

Responsibilities

Own one of these areas end-to-end: Data platform, Core services and APIs, Multi-tenant isolation, Workflow and action runtime, Observability and evals, Safety and blast radius.
Design, implement, and operate a piece of the core stack.
Define the long-term technical direction for your area.
Build systems that real businesses depend on.
Write clean, idiomatic code in at least one of Python, Go, Rust, or TypeScript.
Structure services and defend design choices.
Debug distributed systems issues, including idempotency, partial failure, retry semantics, eventual consistency, schema evolution, and multi-tenant isolation.
Build or operate non-trivial systems on a modern data stack (e.g., Kafka, Spark, dbt, Iceberg, Snowflake, Databricks, BigQuery).
Build for other engineers, ensuring APIs are easy to use correctly and hard to use incorrectly.
Write documentation and make migration paths obvious.
Treat developer experience as a feature.
Make decisions in week 1 that constrain what is possible in year 3.
Build the infrastructure around a model someone else trained.
Have an informed opinion on where ML belongs in the stack and where it does not.
Design autonomy tiers, kill switches, per-action-class blast-radius caps, and audit surfaces.