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Salary: USD 125,000 - 220,000 / annual
General Matter is building the world's lowest-cost uranium enrichment services in the US to power advanced reactors, AI, and critical industries. The company was incubated inside Founders Fund and is backed by over a dozen top venture capital firms.
You will 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, 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. You will play a central role in defining the architecture, schema design, and operational reliability of mission-critical data infrastructure.
Key responsibilities include:
- 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
- Lead debugging, root cause analysis, and reliability improvements across the full ingestion-to-storage stack
Requirements:
- Bachelor's degree or higher in Computer Science, Computer Engineering, Software Engineering, or 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 strong understanding of distributed systems and data reliability
- Experience with high-reliability, on-premise, 24/7 deployments in safety-critical environments
Preferred:
- Experience with time-series databases (InfluxDB, TimescaleDB), in-memory data stores (Redis), and OT/industrial data historians
- Experience with streaming and pub/sub middleware (MQTT, DDS, OPC-UA, Kafka)
- Experience with high-channel-count sensor data and reliability engineering for ingestion
- Background in observability, infrastructure-as-code, and visualization layers (Grafana)
- Background in high-reliability or data-intensive environments (industrial automation, aerospace, energy, finance/trading, telecom, scientific research)
- Demonstrated technical leadership in setting engineering standards and building/mentoring high-performance engineering teams
- Ability to work extended hours and weekends as necessary
