Description
Abnormal AI is looking for a Senior Software Engineer to join the Detection Team. The Detection Division focuses on building advanced technology for identifying and stopping email and cloud-based attacks. As a Senior Software Engineer on the Detection's Signals and Serving Team, you will make feature development at Abnormal fast, responsive, stable, and confident for the ML and Data Science team.
The ideal candidate would have a first-principles approach to building scalable, customer-centric solutions. They should have a drive to solve meaningful and pragmatic problems for real-world people, an ownership and impact-oriented outlook on their efforts and growth, and an ability to iterate in real-time, solving novel problems quickly and autonomously.
Responsibilities
- Architect, design, build, deploy, and maintain Model Serving infrastructure that supports a world-class Detection Engine.
- Own projects that scale model serving and data processing services to handle 10x the traffic served 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 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.
- Excellent problem-solving skills and the ability to work independently in a fast-paced environment.
- Familiarity with machine learning workflows and requirements to support MLE teams effectively.
- Experience with streaming data architectures and real-time processing.
- Knowledge of security and compliance frameworks as they relate to data engineering and data privacy.