Software Engineer 2
Abnormal Security3 months ago
Remote, SingaporeMid Level
H1B Sponsor
Responsibilities
- Build and maintain backend services powering the Critical Email Products product surface.
- Design and evolve event-driven pipelines using Kafka consumers and Airflow DAGs to process enterprise email traffic reliably.
- Work across multi-tenant, multi-cloud deployments serving M365 and Google Workspace customers and FedRAMP environments.
- Use AI-augmented development tools to explore solutions, refine outputs, and deliver reliable production software.
- Collaborate with product and cross-functional partners to deliver features that help customers move from legacy secure email gateways to Abnormal.
- Improve service reliability, observability, and operational quality as the product surface scales.
- Complete a 2–4 hour take-home AI-Augmented Development Challenge within one week when invited after applying.
Requirements
- At least 2 years of professional software development experience is required.
- Backend development experience with Python or Golang is required.
- Experience building scalable, enterprise-grade applications is required.
- Knowledge of AWS, GCP, or Azure and containerization with Docker and Kubernetes is required.
- Strong computer science fundamentals, including data structures and performance optimization, are required.
- Experience with Cursor, GitHub Copilot, or Claude is preferred.
- Experience with Kafka, SQS, or similar event-driven messaging and stream-processing systems is preferred.
- Familiarity with email delivery pipelines, DMARC, DKIM, SPF, or email security products is preferred.
- Exposure to multi-tenant SaaS architectures or M365 and Google Workspace APIs is preferred.
- Experience improving production service reliability, observability, and operational tooling is preferred.
- Prior cybersecurity, email security, or compliance-adjacent experience is preferred.
Tech Stack
Categories
About Abnormal Security
Abnormal AI is the leading AI-native human behavior security platform, leveraging machine learning to stop sophisticated inbound attacks and detect compromised accounts across email and connected applications.