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.