Staff Machine Learning Engineer

Job not on LinkedIn

June 27

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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

• Abnormal Security is looking for a Staff Machine Learning Engineer to join the Message Detection - Attack Detection team. • At Abnormal, we protect our customers against nefarious adversaries who are constantly evolving their techniques and tactics to outwit and undermine the traditional approaches to Security. • In a landscape where a single successful attack can lead to financial losses of millions of dollars, the Attack Detection team plays a central role of building an extremely high recall Detection Engine that can operate on hundreds of millions of messages at milliseconds latency. • This role is central to our mission of protecting the world’s largest enterprises. • You will be responsible for reasoning about the gaps in our multi-layered detection system and proposing generalizable ML solutions. • You will have a significant impact on our technical roadmap, guiding how our diverse set of detection models—spanning behavioral analysis, natural language understanding, and deep learning systems—work in concert. • This is a unique opportunity to shape the future of our ML architecture, from evolving our core training and deployment strategies to defining how our core ML capabilities can be exposed as scalable services to power other products across the company.

🎯 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. • 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

• At Abnormal AI, certain roles are eligible for a bonus, restricted stock units (RSUs), and benefits. • Individual compensation packages are based on factors unique to each candidate, including their skills, experience, qualifications and other job-related reasons. • We know that benefits are also an important piece of your total compensation package. • Learn more about our Compensation and Equity Philosophy on our Benefits & Perks page.