Staff Machine Learning Engineer

Summary

Join Abnormal Security as a Staff Machine Learning Engineer on the Message Detection - Attack Detection team, contributing to a cutting-edge behavioral-based AI system protecting Fortune 1000 companies. You will be responsible for identifying gaps in the multi-layered detection system and developing generalizable ML solutions. This role involves architectural guidance, technical leadership, and mentorship across multiple machine learning workstreams. You will drive the technical roadmap for long-term projects, including evolving model training paradigms and creating centralized ML capabilities. The position requires expertise in the entire ML lifecycle and experience with large-scale model productionization. This is a unique opportunity to shape the future of Abnormal Security's ML architecture.

Requirements

• 8+ years of experience designing and building high-impact, customer-facing machine learning applications
• Proven experience working on ML at scale with direct product impact in mature ML industries such as recommendation systems, ad tech, quantitative finance, or fraud detection
• Strong grasp of the theoretical limitations of deep learning models and a systematic approach to investigating and debugging poor model performance
• Demonstrated experience in the productionization of large-scale ML models in fast-feedback environments
• Ability to reason about abstract system gaps and propose generalizable, architecturally sound ML solutions, not just point fixes
• Expertise across the entire ML lifecycle, from data exploration and feature engineering to model deployment and online scoring
• Fluency in Python and ML frameworks like Scikit-learn, PyTorch, or TensorFlow
• BS degree in Computer Science, Applied Sciences, Information Systems, or a related engineering field

Responsibilities

• Serve as a technical leader and subject matter expert, providing architectural guidance and mentorship across multiple machine learning workstreams
• Architect and design generalizable ML systems to address the most critical gaps in our detection capabilities, moving beyond incremental improvements
• Reason holistically about our entire detection engine, defining the architectural vision for how different classes of models—from heuristic and behavioral to complex deep learning systems—should integrate and operate
• Drive the technical roadmap for foundational, long-term projects, such as evolving our global model training paradigms and creating centralized ML capabilities that can be leveraged as platforms by other teams
• Provide technical mentorship and feedback on ML decisions across different workstreams, elevating the performance of the entire team
• Own the end-to-end ML lifecycle: from data analysis, feature engineering, and model prototyping to working with infrastructure teams on productionization, deployment, and monitoring of large-scale models
• Investigate complex model performance issues, applying a deep theoretical understanding of machine learning and deep learning to diagnose and resolve them
• Continuously adapt our systems to new, unseen attacks by developing and refining our automated model retraining and evaluation pipelines

Preferred Qualifications

• MS or PhD degree in Computer Science, Electrical Engineering, or another related engineering/applied sciences field
• Experience leading multi-quarter, cross-functional ML projects
• Experience with MLOps tools and building scalable data pipelines

Benefits

• Bonus
• Restricted stock units (RSUs)
• Benefits