Software Engineer II - ADEX
Build and operate AI-native cybersecurity detection systems that identify impersonation attacks (brand, domain, VIP) at scale, using Python, LLM agents, and large-scale data pipelines.
- Compensation
- Not disclosed
- Location
- Hybrid - Bangalore, India
- Work setup
- Hybrid
- Seniority
- Middle
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Software engineering
- Posted
Skills
Must have
- 3+ years of professional software engineering experience, with a track record of shipping and operating production systems.
- Strong software engineering fundamentals: data structures, algorithms, system design basics, testing, debugging, and clean, maintainable code.
- Strong Python proficiency and comfort learning new languages and frameworks as needed.
- Solid data-analysis instincts. You are comfortable with SQL and reasoning over large datasets to find signals in noise.
- A detection or adversarial mindset. You enjoy thinking like an attacker, reading real attack samples, and asking, "How would I get past this?"
- Genuine fluency with AI-native development. You already use AI coding agents in your daily work and are excited to build LLM-powered detection, not just consume AI tools.
- Demonstrated ability to own projects that carry some initial ambiguity: clarify and scope loosely defined requirements, make tradeoffs explicit, deliver on time, and communicate status clearly.
- Excellent written and verbal communication, especially in remote, distributed teams. We make decisions in writing.
- A strong growth mindset and sense of ownership.
Nice to have
- Experience with distributed systems, high-throughput pipelines, or large-scale data stores (e.g., PostgreSQL, DynamoDB, Redis, RocksDB, Kafka, Spark, OpenSearch/Elasticsearch).
- Background in security, threat detection, anti-abuse, fraud detection, or trust and safety, particularly systems processing high volumes of email or communication data.
- Experience with ML or LLM evaluation: precision/recall tradeoffs, eval harnesses, prompt iteration.
- Familiarity with domain and DNS concepts (such as typosquatting and homoglyphs) or with identity and impersonation signals.
- Experience with large-scale data tooling (e.g., Databricks, Spark, Airflow) and distributed pipelines.
- Experience with containerization and orchestration (Docker, Kubernetes) and infrastructure-as-code tooling.
- Familiarity with modern frontend frameworks (e.g., React) for full-stack roles, or with ML/ML Ops for Detection/MLE-focused roles.
- Prior experience in a fast-paced, high-growth startup environment where you’ve had to balance speed, quality, and ambiguity.
Full description
About the Role
Enterprises of all sizes trust Abnormal's AI-native security products to stop cybercrime and protect critical communications, identities, and infrastructure in the cloud. Our products are data- and systems-intensive, operating at high scale and low latency across multiple clouds and regions.
As a Software Engineer II on the ADEX Team, you are a highly capable detection feature owner: you take a detection problem, come up with an idea, design a technical approach, and drive it end-to-end, from design and implementation through launch, operation, and continuous improvement. You will work with a world-class group of engineers, product managers, and data scientists to build and operate detection that is reliable, scalable, and AI-native by default.
This role focuses on impersonation detection, including brand, lookalike-domain, VIP and employee impersonation. It is ideal for an engineer who has already shipped meaningful production systems, wants more ownership and impact, and is excited to use AI to build detection that was not possible before.
About the Team
The Advanced Detection of EXtended threats (ADEX) is an attack-detection team inside Abnormal's Detection org. We own several of the highest-visibility detection surfaces at the company, spanning attachment-based attacks, fraud, and impersonation. We work the way an analyst would: we study the attacks that get through, understand the underlying pattern, and translate it into system-level detection enhancements that generalize beyond the individual attack.
We are also one of the most AI-forward teams at Abnormal. We build and operate LLM-based detection agents and treat internal AI tooling as a first-class deliverable. Every engineer here writes detection logic and builds AI agents. Impersonation is one of the most damaging and visible classes of attack we defend against, where even simple attacks that slip through erode customer trust, so this is a surface we hold to a very high bar.
What You'll Do
- Design, build, and operate detection that is core to Abnormal's products, from initial design through rollout, monitoring, and ongoing maintenance.
- Own detection projects end-to-end, including those that begin with a degree of ambiguity: scope loosely defined problems, identify risks, define milestones, and deliver reliably.
- Analyze attacks that get through. Pull and study missed-attack data, read the messages the way an attacker and an analyst would, identify the underlying pattern, and translate it into detection enhancements or entirely new detection systems.
- Write and tune detection logic using scored signals and attributes, add new signals across the pipeline, and drive changes to launch with a strong focus on minimizing false positives.