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Salary: USD 149,200 - 214,500 / annual
Abnormal AI is seeking a Software Engineer II to join the Detection Team, which builds advanced technology to identify and stop email and cloud-based attacks. In this role, you will own the Model Platform infrastructure that supports the Detection Engine, making feature development fast, responsive, stable, and confident for ML and Data Science teams.
Key responsibilities include architecting, designing, building, deploying, and maintaining model serving infrastructure using industry-standard AI tools. You will own projects that scale model serving and data processing services to handle 10x current traffic levels, manage real-time and near real-time streaming pipelines, and operate online feature serving services. You'll collaborate closely with machine learning engineers and data scientists, translating their feedback into strategic execution.
Required qualifications: 4+ years as a Software Engineer or similar role with hands-on experience building data-focused solutions. You must be proficient with AI tools to accelerate engineering outcomes through discovery, design, implementation, and rollout. Experience maintaining large-scale distributed systems on AWS, GCP, or Azure is essential, along with strong cloud engineering best practices. You need hands-on experience with real-time and near real-time data pipelines or streaming services, strong computer science fundamentals, data structures knowledge, and performance optimization skills. A BS in Computer Science, Applied Sciences, Information Systems, or related engineering field is required.
Nice-to-have skills include familiarity with AWS, Kubernetes, Python/Django, Golang, and Postgres; experience building scalable enterprise-grade applications; web security knowledge (OWASP Top 10); and familiarity with AI development tools like Cursor, GitHub Copilot, or Claude.
The ideal candidate brings a first-principles approach to building scalable, customer-centric solutions, an ownership-oriented mindset, and the ability to iterate quickly and autonomously on novel problems.
