Design, build, launch, monitor, and maintain reliable, scalable detection systems.
Own detection projects end-to-end, including scoping ambiguous problems, defining milestones, and managing delivery.
Analyze missed attacks and translate underlying patterns into detection enhancements or new detection systems.
Write and tune detection logic, add signals across the pipeline, and optimize for low false-positive rates.
Build and rigorously evaluate LLM-based detection agents using precision and recall measurements.
Expose detection results as reusable intelligence for other products and teams.
Participate in on-call rotations, resolve customer escalations, and improve observability and runbooks.
Use AI-assisted development tools for code, tests, analysis, experiments, and documentation while validating results.
Document work, share learnings, and contribute to code and design reviews.
Requirements
At least 3 years of professional software engineering experience shipping and operating production systems.
Strong fundamentals in data structures, algorithms, system design, testing, debugging, and maintainable software development.
Strong Python proficiency and ability to learn new languages and frameworks.
Comfort using SQL and analyzing large datasets to identify meaningful signals.
Detection or adversarial mindset, including the ability to reason about attacks and evasion.
Experience using AI coding agents and interest in building LLM-powered detection systems.
Ability to own ambiguous projects, make tradeoffs, deliver on schedule, and communicate clearly.
Excellent written and verbal communication skills for remote, distributed collaboration.
Experience with distributed systems, high-throughput pipelines, or large-scale data stores is preferred.
Background in security, threat detection, anti-abuse, fraud detection, or trust and safety is preferred.
Experience with ML or LLM evaluation, precision/recall tradeoffs, evaluation harnesses, or prompt iteration is preferred.
Familiarity with domain and DNS concepts, identity and impersonation signals, large-scale data tooling, containerization, orchestration, infrastructure-as-code, frontend frameworks, or ML/ML Ops is preferred.
Startup experience balancing speed, quality, and ambiguity is preferred.
Benefits
Work on meaningful AI, security, and large-scale detection problems with direct customer impact.
Collaborate with smart, supportive, and ambitious teammates focused on learning and detection quality.
Receive ownership and autonomy over an important detection surface with opportunities to grow toward Senior and Staff roles.
Join an AI-native R&D organization with investment in AI tools, workflows, and training.
Participate in a remote, distributed work environment.
The recruiting process uses AI-assisted tools to help prepare interview questions, but hiring decisions are made by people; successful candidates undergo legally compliant pre-employment checks.
Abnormal Security
Abnormal AI is the leading AI-native human behavior security platform, leveraging machine learning to stop sophisticated inbound attacks and detect compromised accounts across email and connected applications.