Threat Detection Engineer – Security Operations

Company Overview ID.me is the next-generation digital identity wallet that simplifies how individuals securely prove their identity online. Consumers can verify their identity with ID.me once and seamlessly login across websites without having to create a new login and verify their identity again. Over 152 million users experience streamlined login and identity verification with ID.me at 20 federal agencies, 45 state government agencies, and 70+ healthcare organizations. More than 600+ consumer brands use ID.me to verify communities and user segments to honor service and build more authentic relationships. ID.me’s technology meets the federal standards for consumer authentication set by the Commerce Department and is approved as a NIST 800-63-3 IAL2 / AAL2 credential service provider by the Kantara Initiative. ID.me is committed to “No Identity Left Behind” to enable all people to have a secure digital identity. To learn more, visit https://network.id.me/ . Role Summary We are seeking a Threat Detection Engineer to join our security engineering and operations team. In this role, you will develop, test, and optimize high-fidelity detections across modern security data platforms, with a focus on security analytics, automation, and threat detection at scale. You will be expected to bring — and continuously develop — strong AI literacy: designing detection workflows that leverage large language models, anomaly detection, and agentic pipelines, while also understanding and defending against AI-specific attack surfaces. You should be comfortable writing structured, reusable detection logic, working with infrastructure-as-code (IaC), and integrating behavioral and threat intelligence into detection strategies. You will collaborate closely with incident response, threat intel, and platform engineering teams to ensure resilient, high-quality coverage of modern threat scenarios across cloud and enterprise environments — including threats targeting and exploiting AI systems. Key Responsibilities - Design and implement detection logic across SIEM/SOAR platforms, including Splunk, Google Chronicle (SecOps), and Elastic/Logstash. - Build scalable detection rules, analytics, and anomaly models to detect adversary TTPs aligned with MITRE ATT&CK. - Develop and maintain detection-as-code using Python and YAML-based rule formats (e.g., Sigma, YARA-L, Kusto, or Lucene). - Design and evaluate LLM-assisted detection and triage workflows, including prompt engineering for alert enrichment, summarization, and classification. - Build and maintain AI-augmented detection pipelines: anomaly scoring, embedding-based similarity search, natural language parsing for phishing and social engineering detection, and LLM-based log analysis. - Apply AI security literacy to identify and detect risks in AI-integrated environments, including prompt injection, model abuse, data exfiltration via LLMs, and shadow AI usage. - Perform quality assurance and validation of alerts — including AI-generated signals — to minimize false positives and increase signal fidelity. - Leverage Snowflake and SQL to normalize and query large datasets across multiple telemetry sources, including AI system logs and API call records. - Contribute to infrastructure-as-code workflows for detection deployment (e.g., Terraform, GitOps pipelines). - Collaborate with Threat Intelligence and IR teams to translate threat actor TTPs — including those targeting AI systems — into actionable detections. - Participate in detection tuning, red/blue team exercises, and post-incident reviews, including adversarial testing of AI-assisted detection logic. - Maintain availability for 24x7 on-call rotation and ensure timely response to security incidents during standard EST business hours. Required Qualifications - 2-4 years in a security engineering or other relevant security operations role. - Proficiency with Splunk, Elastic Stack, Google SecOps (Chronicle), and/or Logstash. - Strong programming or scripting experience in Python and SQL. - Working experience authoring detection logic using YARA-L, Sigma, or equivalent formats. - Demonstrated AI literacy: hands-on experience using LLM APIs (e.g., OpenAI, Anthropic, Google Gemini) or AI/ML frameworks for security use cases, including prompt engineering, retrieval-augmented generation (RAG), or agentic workflows. - Understanding of AI/ML concepts relevant to detection: anomaly detection, clustering, embedding models, LLM-based enrichment, and the limitations and failure modes of these approaches. - Ability to assess and detect AI-specific threats: prompt injection, model inversion, training data poisoning, and LLM-facilitated social engineering. - Experience working with cloud-scale security data and log management tools. - Familiarity with MITRE ATT&CK, threat modeling, and behavioral-based detections. - Knowledge of Infrastructure-as-Code (IaC) and version control systems (e.g., GitHub, Terraform, GitLab CI/CD). Preferred Qualifications - Industry security certifications such as GCIA, GCIH, GCFA, Security+, or AI/ML security credentials. - Experience with Google Cloud Platform (GCP) and Google Kubernetes Engine (GKE), including GKE security posture management, audit logging, and cloud-native workload monitoring. - Experience building or operating SOAR integrations with LLM-assisted triage or response recommendations. - Hands-on experience with agentic AI frameworks (e.g., LangChain, LlamaIndex, or custom tool-use pipelines) applied to security automation. - Familiarity with Snowflake's Security Data Lake or cloud-native log pipelines, including telemetry from AI platforms (e.g., OpenAI API logs, Azure AI services). - Exposure to red team/blue team collaboration, threat hunting, or adversary emulation frameworks, with emphasis on AI-enabled attack scenarios. - Experience red-teaming or evaluating LLM-based systems for security weaknesses. - Contr