Software Engineer 2 - Identity Security

Abnormal

20d•$148,800 - $175,000

About The Position

Enterprises are undergoing two simultaneous transformations: moving critical infrastructure to the cloud and adopting AI agents at scale. Both create an identity explosion—humans, service accounts, and AI agents now outnumber traditional users 10-to-1, each with permissions that legacy tools can't govern. Abnormal's Identity Security team is building the platform to secure this new reality: unified visibility across all identity types, behavioral intelligence that understands what's normal, and AI-native automation that operates at cloud speed. We're extending the same behavioral approach that made Abnormal the leader in email security to protect identities across cloud infrastructure and AI systems.

Requirements

2+ years building software applications.
Experience productionizing large‑scale, data‑intensive systems.
High velocity and creativity in solving technical challenges.
Experience & desire to adopt & improve AI-native development workflows.
Strong debugging with logs, metrics, and signals.
Ability to translate business requirements into high‑quality software.
Ability to independently solve complex problems and work cross‑functionally.
BS in CS/SE/IS or related field.

Nice To Haves

Experience with Go and Python.
Experience in IAM, non‑human/machine identities, secrets management, workload identities, OAuth/OIDC, SCIM, SSO, or policy engines.
Experience in cybersecurity or related industries.
Experience with big data, statistics, and ML for identity/behavioral risk modeling.

Responsibilities

Build discovery, classification, monitoring, and protection for machine identities and their agentic actions.
Ship guardrails and policy engines that enable safe automation while preventing misuse, escalation, and anomalous behavior.
Create high‑availability pipelines that ingest and correlate identity, authorization, and behavioral signals from cloud, SaaS, IAM/SSO, and audit logs.
Drive 0→1 iteration: prototype quickly, test assumptions, learn from usage, and scale simple solutions.
Collaborate across security, platform, and data teams; write and review technical designs; and participate in core SDLC rituals.