About Us:
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like
Ramp
, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We've passed $1B in annualized run rate, up 15x from a year ago. In October 2026 we raised a $750M Series D led by Accel at a $15.75B valuation.
Our team includes creators of popular open-source projects (e.g.,
Seaborn
,
Luigi
), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
The Role:
We’re looking for a Detection & Response Engineer to build the systems that help us identify, investigate, and respond to threats across our platform.
This is an engineering role focused on automation. You’ll build detections, investigation tooling, and response capabilities that scale with our infrastructure, using AI where it meaningfully improves signal, investigation speed, and operational effectiveness.
You’ll work closely with infrastructure, platform, and security engineers to ensure every incident makes the platform more resilient.
What You'll Work On:
Detection Engineering
Design and build high-fidelity detections for attacks, abuse, and anomalous behavior across our infrastructure and production systems
Continuously improve detections based on telemetry, threat intelligence, and lessons learned from incidents
Improve visibility across cloud infrastructure, containers, identity systems, and production services
Incident Response
Lead or participate in investigations spanning production infrastructure, cloud environments, and internal systems
Build playbooks and automation that reduce investigation time and improve response consistency
Drive post-incident improvements that eliminate entire classes of future incidents
Security Tooling & Automation
Build internal tooling that improves detection, investigation, and response workflows
Leverage LLMs to automate repetitive analysis, accelerate investigations, and surface actionable insights from security telemetry
Improve the collection, quality, and usability of security telemetry across the platform
Engineering Partnership
Partner with engineering teams to ensure new systems are observable and secure by default
Help teams instrument services with the telemetry needed for effective detection and response
Drive security improvements that make the platform easier to defend over time
You May Be a Fit If:
You have experience in detection engineering, incident response, security engineering, or software engineering with a strong security focus
You have strong software engineering skills and experience building production systems.
You’ve investigated security incidents in cloud-native or distributed environments
You’re familiar with modern cloud infrastructure, Kubernetes, Linux, and networking
You’ve built detections using logs, telemetry, behavioral signals, or large-scale event data.
You have strong SQL skills for investigating security events and developing detections
You’re interested in applying AI and LLMs to detection, investigation, and response, and in understanding emerging threats involving AI-powered systems
You have strong written and verbal communication skills
Nice-to-Have:
Experience building AI- or LLM-powered security tooling
Experience with SIEM, SOAR, or EDR platforms
Experience with Kubernetes security or large-scale cloud infrastructure
Experience with threat hunting, malware analysis, or digital forensics
Experience contributing to security operations in a high-growth engineering organization