Software Engineer, Data Platform

General Matter•Los Angeles, CA

3d•Onsite

About The Position

General Matter enriches uranium. We are designing, building, and operating the world’s lowest-cost enrichment services, here in the US. In the process, we are restoring America’s ability to produce nuclear fuel to power AI, advanced manufacturing and critical industries our country relies on to remain competitive. Fuel drives the cost of advanced reactor electricity production, and enrichment drives the cost of the fuel that advanced reactors consume. Reliable, low-cost enrichment is the catalyst for the nuclear Renaissance now under way. Our mission is to make nuclear not only the cleanest and safest source of baseload power, but also the most affordable. We believe abundant nuclear energy will lead to a post-scarcity society. We were incubated inside Founders Fund, like Anduril and Palantir before us, and are backed by over a dozen of the world's top venture capital firms. Our lean, world-class team of engineers and operators is applying a first-principles approach to solving the problem of nuclear fuel production. We are a mission-driven company with a culture of urgency, accountability, and transparency. Help us build a high-energy society by making the cleanest, safest form of baseload energy the most affordable. About This Role We are seeking a Data Platform Software Engineer to design and implement the data acquisition, storage, and streaming backbone for test stands and production systems. This role sits at the intersection of industrial data systems and high-performance backend engineering, and is responsible for building reliable, scalable data pipelines that ingest high-throughput, high-channel-count sensor telemetry without loss and serve it to visualization and analytics layers. The successful candidate will play a central role in defining the architecture, schema design, and operational reliability of mission-critical data infrastructure.

Requirements

Bachelor’s degree or higher in Computer Science, Computer Engineering, Software Engineering, or a related field.
5+ years of experience building data pipelines, streaming systems, or backend data infrastructure.
Strong, production-level experience in Python and SQL.
Hands-on experience with high-throughput data ingestion and time-series or historian storage, with a strong understanding of distributed systems and data reliability.
Experience with high-reliability, on-premise, 24/7 deployments in safety-critical environments.

Nice To Haves

Experience with time-series databases (e.g., InfluxDB, TimescaleDB), in-memory data stores (e.g., Redis), and OT/industrial data historians.
Experience with streaming and pub/sub middleware (e.g., MQTT, DDS, OPC-UA, Kafka).
Experience with high-channel-count sensor data and reliability engineering for ingestion.
Background in observability, infrastructure-as-code, and feeding visualization layers (e.g., Grafana).
Background in high-reliability or data-intensive environments (e.g., industrial automation, aerospace, energy, finance/trading, telecom, scientific research).
Demonstrated technical leadership in setting engineering standards, and building and mentoring high-performance engineering teams.

Responsibilities

Design, implement, and maintain high-throughput data pipelines for kHz-class, high-channel-count sensor telemetry across test and production environments.
Design and operate time-series storage and industrial data historians for long-term retention, query performance, and schema evolution.
Build ingestion and streaming services with a focus on buffering, backpressure, idempotency, and no silent data loss.
Define and champion modern software engineering practices, data architecture, schema standards, testing strategies, and operational best practices across the data platform.
Work closely with controls, test automation, and visualization teams to feed downstream analytics and live dashboards directly.
Lead debugging, root cause analysis, and reliability improvements across the full ingestion-to-storage stack.