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Abnormal AI is seeking a Senior Software Engineer to join the Detection Team, which builds advanced technology for identifying and stopping email and cloud-based attacks. You will work on the Detection Division's Signals and Serving Team, making feature development fast, responsive, stable, and confident for ML and Data Science teams.
Key responsibilities include:
- Architect, design, build, deploy, and maintain Model Serving infrastructure supporting a world-class Detection Engine
- Own projects that scale model serving and data processing services to handle 10x current traffic
- Build platform infrastructure for fighting rapidly generated AI attacks
- Own real-time and near real-time streaming pipelines and online feature serving services
- Build Abnormal's ML Training platform to improve MLE velocity and product precision/recall
- Collaborate closely with ML engineers and Data Science teams by translating feedback into strategy and execution
- Coach and mentor junior engineers through 1-on-1s, pair programming, code reviews, and design reviews
You should have a first-principles approach to building scalable, customer-centric solutions; drive to solve meaningful, pragmatic problems; ownership and impact-oriented outlook; and ability to iterate quickly while solving novel problems autonomously.
Requirements:
- 5+ years of experience as a Software Engineer or similar role, with hands-on experience building ML-engineering-focused solutions
- Experience maintaining large-scale distributed systems on cloud platforms (AWS, GCP, or Azure) with strong grasp of cloud-based engineering best practices
- Experience maintaining real-time and near real-time data pipelines or streaming services at high scale
- Proven ability to collaborate effectively with cross-functional teams (data scientists, ML engineers, product managers, stakeholders); can translate requirements into actionable technical tasks, communicate progress clearly, and adapt to feedback
- Excellent problem-solving skills and ability to work independently in fast-paced environment; can break down complex challenges into manageable steps and balance immediate needs with long-term scalability
- Familiarity with machine learning workflows and requirements to support MLE teams, including feature development and serving at 50K+ QPS, offline/online equivalency, and 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
