Senior Software Engineer

At Abnormal Security, we are on a thrilling mission to safeguard the world's largest enterprises against a vast range of relentless email attacks. We protect our customers against adversaries who are constantly evolving their techniques and tactics to outwit and undermine the traditional approaches to Email Security. That’s what makes our novel behavioral-based approach so…Abnormal. Abnormal has constantly been named as one of the top cybersecurity startups and our behavioral AI system has helped us win various cybersecurity accolades resulting in being trusted to protect more than 15% of the Fortune 500 (and ever growing).

As a Senior Software Engineer on our team, you'll be at the forefront of building automated systems and tools that deliver world-class detection efficacy for every customer, in every region.

You'll design, implement, and maintain scalable, efficient systems that enable precise diagnosis and treatment of efficacy issues, both for our core detection rules and at a granular, per-customer level. A key part of your role will involve developing frameworks to identify, diagnose, and correct efficacy performance problems across all customer accounts.

Your work will directly contribute to creating automated solutions for resolving misclassifications and building systems that can automatically identify and disable detectors that no longer provide value. Ultimately, your contributions will be crucial in enabling per-customer guarantees on detection efficacy for both recall and precision, directly impacting the success of our customers and the company as a whole. You'll collaborate closely with ML Engineers, Data Scientists, and fellow Software Engineers to achieve these critical goals.

• Design and implement automated systems for diagnosing and treating efficacy issues, both for core detection rules and at a per-customer level.
• Develop frameworks and tools to identify, diagnose, and correct efficacy performance issues across all customer accounts.
• Build scalable systems for efficient misclassification resolution, applicable to both general rules and individual customer configurations.
• Create automated mechanisms to identify and disable detectors that are no longer cost-effective or have degraded in value.
• Establish and enable per-customer guarantees on detection efficacy for recall and precision.
• Develop tools and mechanisms that provide deep insight into per-customer detection efficacy and generate actionable steps to maintain high performance.
• Ensure the scalability and extensibility of our efficacy automation infrastructure to support an increasing number of customers, diverse data, and evolving detection strategies.
• Write code with testability, readability, edge cases, and errors in mind, biasing towards simple, iterative solutions.
• Write and review technical design documents for new systems and features.
• Participate in sprint planning, code reviews, standups, and other aspects of the software development life cycle.

• Strong Programming Skills: 5+ years of expertise in one or more relevant languages like Python, Java, Go, or Scala focused on  building scalable, maintainable, and robust systems.
• Distributed Systems Design and Implementation: Deep understanding and experience with building and operating highly available, scalable, and fault-tolerant distributed systems. This includes concepts like microservices, message queues (Kafka, RabbitMQ), distributed databases, caching, and load balancing.
• Database Management: Proficiency with various database technologies (SQL, NoSQL) for efficient data storage, retrieval, and management, especially in a distributed context.
• Problem-Solving & Analytical Thinking: The ability to break down complex problems, identify root causes, and devise creative and effective solutions, particularly when dealing with intricate efficacy issues.
• Proactive & Ownership Mindset: Taking initiative, owning projects from conception to deployment, and being accountable for their success.
• Collaboration & Communication: Excellent communication skills (written and verbal) to effectively collaborate with ML Engineers, Data Scientists, Product Managers, and other stakeholders. This includes explaining complex technical concepts to non-technical audiences.
• B.Sc Degree (or higher): Computer Science, Software Engineering, Information Systems or other related engineering field

• Understanding of ML Concepts: While not necessarily an ML Engineer, a strong conceptual understanding of machine learning principles, model evaluation metrics (precision, recall, false positives, false negatives), and how models interact with data is beneficial.
• Feature Engineering (Conceptual): Understanding how features are derived and their impact on detection efficacy.
• Big Data Technologies: Familiarity with big data processing frameworks like Apache Spark, Hadoop, Flink, or similar, for handling and analyzing massive volumes of detection data.
• Data Pipelines (ETL/ELT): Expertise in designing, building, and maintaining robust and automated data pipelines for ingesting, transforming, and loading large datasets.
• Experience in the cybersecurity industry, financial fraud, application security, or related industries

#LI-RT1