About us
We’re a SF Bay Area Cyber AI startup. We’ve raised funding from top tier investors. Our vision is simple: enable all security teams, to perform security operations with the efficiency and effectiveness needed to prevent breaches . We’re a small team of researchers and engineers with a deep focus in cyber and AI. Our product automates the triage for any security alert leveraging deep research, big data and dozens of AI Agents.
Join us and boost your career with hands-on AI experience.
The Role
As a Staff Software Engineer at Radiant Security, you will be responsible for building and optimizing high-performance data platforms that support efficient data ingestion and querying at scale. You'll focus on developing software solutions that ensure the system’s reliability, monitoring, and scalability, working closely with cross-functional teams to address data challenges head-on.
What you'll do
- Design and implement scalable data ingestion pipelines, ensuring efficient and reliable data flow from diverse sources.
- Build out core platform tooling, monitoring, and engineering solutions that the rest of the company can leverage for their own performant apps.
- Collaborate with engineering teams to integrate data solutions into the broader platform, ensuring system-wide performance optimization.
- Troubleshoot and enhance our data pipelines and ingestion systems, ensuring minimal downtime and high reliability.
- Optimize performance in large-scale datasets by applying best practices in indexing, caching, and partitioning.
Things we're looking for
- Solid programming skills in languages like Python, Golang, and NodeJS with experience in building scalable, high-performance data processing solutions.
- Familiarity with cloud infrastructure (AWS, GCP, or Azure) and their respective data services (e.g., Redshift, BigQuery).
- Experience building data pipelines using tools such as Apache Kafka, AWS Kinesis, or similar technologies.
- Knowledge of distributed systems and techniques for building fault-tolerant, high-availability platforms.
- Strong understanding of various database and search technologies (e.g. SQL, ElasticSearch, MongoDB) and performance tuning for large datasets, using distributed query engines (e.g., Presto, Snowflake).
- Proven experience in monitoring and troubleshooting data platforms, using tools like Prometheus, Grafana, or Datadog.
- Hands-on experience in containerization and orchestration (Docker, Kubernetes) for deploying scalable data services.
The process
People Screening > Hiring Manager Interview > Technical Interviews > Executive Interview
