Role summary
Radiant Security is seeking a Staff Software Engineer to own the full lifecycle of customer security telemetry, from ingestion to storage in their AI SOC platform. This role is critical for ensuring the reliability and scalability of their ingestion infrastructure and data lake architecture. You will define and evolve the data lake, establish DevOps practices, and drive technical leadership. The ideal candidate has strong backend and data systems experience, expertise in cloud, streaming, and data platforms, and a deep understanding of production-grade infrastructure and reliability practices. Experience with distributed systems, containerization, and technical leadership is essential.
About us
Radiant Security is building the most advanced AI SOC platform, featuring unbounded alert triage, investigation, and response for security teams at scale. Our platform ingests alerts from across an organization's entire security stack (SIEM, EDR, identity, cloud) and uses AI to triage, investigate, and surface what actually matters. We're replacing alert fatigue with clear signal, so analysts can focus on real threats.We're a small, fast-moving team. We ship continuously, stay close to customers, and hold ourselves to a high standard. Our product touches the daily workflows of security teams, and decisions we make have a direct impact on how quickly threats get resolved.Join us and boost your career with hands-on AI experience.
The Role
As a Staff Software Engineer at Radiant Security, you’ll own the full lifecycle of customer security telemetry — from ingestion to storage in our data lake.When customers face active incidents, our ingestion pipeline is mission-critical. Reliability and operational excellence here are product requirements, not just engineering ideals.You’ll drive the scalability and reliability of our ingestion infrastructure, define the architecture of our data lake, and establish the DevOps practices that allow a lean team to evolve safely over time.
What you'll do
Own and scale our ingestion platform end-to-endDesign and operate high-throughput ingestion pipelines with zero-downtime deployment patterns (dual-write, backfills, safe rollback), ensuring resilience under real-world failure modes (backpressure under load spikes, delivery guarantees, DLQs, replay mechanisms) and enforcing strict tenant isolation (per-tenant rate limiting, noisy neighbor prevention, storage partitioning across pipeline and lake layers)
Define and evolve our data lake architectureOwn storage layout, partitioning, schema design, and ensuring efficient high-throughput writes and reliable downstream consumption, while managing lifecycle (compaction, retention, cold storage, cost optimization)
Build and operationalize platform foundationsDevelop deployment pipelines for stateful services, per-tenant quota systems, synthetic load testing, and monitoring that the broader engineering team depends on
Establish reliability standards and operate in productionDefine and enforce SLOs (latency, durability, availability), including alerting, and incident response, while continuously improving observability and operational excellence
Drive technical leadership and platform strategyPartner with product and engineering leadership to translate strategic goals into clear requirements and execution plans, while mentoring engineers, setting technical direction, and raising the bar on design, reliability, and operational excellence across the team
Things we're looking for
Strong backend and data systems experienceProven experience building and operating high-throughput ingestion systems in production, with strong backend programming skills (our stack uses Python, Golang, and Node.js)
Cloud, streaming, and data platform expertise Experience with AWS, GCP, or Azure (S3, GCS, Data Lake), streaming systems (Kafka, Kinesis — including delivery semantics and consumer group management), and large-scale data lake design (partitioning, formats, lifecycle)
Production-grade infrastructure and reliability practices Experience with zero-downtime migrations (dual-write, backfills, safe cutovers), Infrastructure as Code (Terraform, Pulumi), CI/CD (canary + rollback), and operating and monitoring data platforms in production (Prometheus, Grafana, Datadog), including SLO definition and incident response
Strong distributed systems and storage fundamentals Fault tolerance, backpressure handling, graceful degradation, partition tolerance, plus experience with databases, object storage, and performance tuning for high-throughput workloads
Modern infrastructure stack experienceContainerization and orchestration (Docker, Kubernetes) for deploying and scaling stateful service
The process
Application Review > People Screening > Hiring Manager Interview > Technical Interviews > Executive Interview
