Remote
Senior Software Engineer - Model Platform
About this role
About the Role Abnormal AI is looking for a Senior Software Engineer to join the Detection Team. The Detection Division is focused on building the world’s most advanced technology for identifying and stopping email and cloud-based attacks that were previously undetectable and helping make the world a safer place. As a Senior Software Engineer building systems for Detection’s Signals and Serving Team, you will make feature development at Abnormal fast, responsive, stable, and confident for our ML and Data Science team.
The ideal candidate would have the following qualities: A first principles approach to building scalable, customer-centric solutions A drive to solve meaningful & pragmatic problems for real-world people An ownership and impact-oriented outlook on your efforts and growth An ability to iterate in real-time-solving novel problems, quickly and autonomously An ability to iterate in real-time - solving novel problems, quickly and autonomously What you will do Architect, design, build, deploy, and maintain Model Serving infrastructure that supports a world-class Detection Engine Own projects that scale our model serving and data processing services to handle 10x the traffic we serve today Build the platform for fighting against rapidly generated AI attacks Own real-time, near real-time streaming pipelines, and online feature serving services Build Abnormal’s ML Training platform, improving MLE velocity and product precision and recall Collaborate closely with MLE and Data Science teams by distilling feedback, correlating it to strategy, and executing Coach and mentor junior engineers via 1on1s, pair programming, high-quality code reviews, and design reviews Must Haves 5+ years of experience as a Software Engineer or in a similar role, with hands-on experience in building ML-engineering focused solutions.
Experience maintaining large-scale distributed systems on cloud platforms such as AWS, GCP, or Azure, including a strong grasp of cloud-based engineering best practices. Experience with maintaining real-time and near real-time data pipelines or streaming services at high scale Proven ability to collaborate effectively with cross-functional teams, including data scientists, machine learning engineers, product managers, and other stakeholders.