Senior Software Engineer - Model Platform
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. You can translate requirements into actionable technical tasks, communicate progress clearly, and adapt to feedback.
Excellent problem-solving skills and the ability to work independently in a fast-paced environment. You can break down complex challenges into manageable steps and iterate on solutions, balancing immediate needs with long-term scalability.
Familiarity with machine learning workflows and requirements to support MLE teams effectively. This includes feature development and serving at 50K+ QPS, offline/online equivalency, large batch jobs for data gathering and training of tree and deep learning models.
Experience with streaming data architectures and real-time processing.
Knowledge of security and compliance frameworks as they relate to data engineering and data privacy.
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