Senior Software Engineer - Model Platform

Abnormal AI is focused on identifying and stopping previously undetectable email and cloud-based attacks. The Senior Software Engineer will build and scale model-serving infrastructure, real-time data pipelines, online feature-serving services, and machine learning training platforms while partnering with ML Engineering and Data Science teams.

Responsibilities:

• 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

Requirements:

• 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
• 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