Staff Machine Learning Engineer – Message Security Detection

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October 30

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Abnormal Security

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Abnormal provides total protection against the widest range of attacks including phishing, malware, ransomware, social engineering, executive impersonation, supply chain compromise, internal account compromise, spam, and graymail.

501 - 1000 employees

📋 Description

• 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.

🎯 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.

🏖️ Benefits

• Abnormal AI is an equal opportunity employer • Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status or other characteristics protected by law