Senior Software Engineer - Model Platform

Abnormal AI develops technology to identify and stop previously undetectable email and cloud-based attacks. The Senior Software Engineer will build and scale model serving, data processing, streaming, and ML training platforms that support the Detection Engine and improve MLE and Data Science productivity. The role also involves cross-functional collaboration and mentoring junior engineers.

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:

• • 5+ years of experience as a Software Engineer or in a similar role, with hands-on experience in building ML-engineering focused solutions
• Experience maintaininglarge-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