Cloud Engineer III, Exposure Management (Hybrid, Sunnyvale)

CrowdStrike•Sunnyvale, CA

1d•Hybrid

About The Position

This is a backend engineering position on CrowdStrike’s Spotlight platform team. The engineer will design, build, and operate cloud-native services and infrastructure that power endpoint vulnerability assessment capabilities at global scale, with an emphasis on distributed systems, service reliability, scalability, and cloud architecture across AWS and GCP. The work involves reasoning and judgment over large volumes of continuously changing data, including decisions about where agentic reasoning is appropriate, where deterministic approaches are preferable, and how to operate either safely and reliably in production. Falcon Vulnerability Assessment is a scan-less endpoint vulnerability assessment solution that provides real-time, zero-impact assessments of endpoint security posture. The Falcon platform combines endpoint sensors—which observe system activity, recognize malicious behavior, provide on-box prevention, and send telemetry to the cloud—with cloud services that aggregate and correlate telemetry across customer networks and present prioritized threats to customers’ operations teams. The role builds high-throughput, low-latency backend services and APIs; operates cloud infrastructure and event-streaming pipelines; and owns service reliability, performance, and production incident response. It also develops agentic product capabilities and takes AI capabilities from prototype to production, with responsibility for latency, cost, correctness, evaluation, and enterprise safety guardrails. The position is hybrid in Sunnyvale.

Requirements

At least 7 years of backend engineering experience.
Solid production experience with Golang or Java.
Strong understanding of distributed systems, microservices, and event-driven architectures.
Hands-on experience with AWS cloud services such as S3 and SQS.
Experience with Kafka or other event-streaming platforms.
Strong database skills with Cassandra, Elasticsearch/OpenSearch, Redis, or similar distributed data stores.
Hands-on use of frontier-model SDKs such as Anthropic or OpenAI, and AI-assisted development tools, with a realistic understanding of their capabilities and limitations.
Proven experience using AI technologies to enhance decision-making, streamline workflows and processes, improve efficiency, and drive business outcomes.
Ability to communicate effectively at all levels of an organization.
Strong collaboration skills and experience working in a global team.
Commitment to software quality, including unit testing, code review, and continuous integration.

Nice To Haves

Experience in cybersecurity; welcomed but not required.
Experience with Kubernetes and event-driven autoscaling such as KEDA.
Experience with observability tooling such as Splunk, Grafana, or LogScale.
Familiarity with security vulnerability management or cybersecurity platforms.
Good understanding of Windows OS internals and the Windows API.
Experience with infrastructure as code such as Terraform or CDK.
Prior experience with large-scale data acquisition or content-pipeline systems.
Experience with evaluation and observability tooling for agentic systems.
Experience with RAG pipelines, vector stores, or retrieval-augmented agents.
Experience with graph databases and complex data modeling.

Responsibilities

Design and build high-throughput, low-latency backend services and APIs in Golang and Python.
Architect and operate cloud-native infrastructure across AWS and GCP, including S3, SQS, Kubernetes, and KEDA.
Build and maintain Kafka-based event-streaming pipelines for near-real-time vulnerability event processing.
Drive backend engineering and process automation across the platform.
Partner with security researchers and platform teams, including Intel and EPP, to deliver integrated capabilities.
Own service reliability, including performance profiling, incident response, and root-cause analysis of time-sensitive production issues.
Continuously improve architecture, performance, and stability through re-engineering.
Write high-quality, well-tested, production-ready code focused on observability and operability.
Build agentic capabilities that reason over real data, use tools, and produce explainable results customers can act on.
Take AI capabilities from prototype to production, owning latency, cost, correctness, and evaluation.
Design guardrails and policies that make autonomous reasoning safe for enterprise customers.