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 Lovable, 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 recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
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.
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.
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
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
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
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
Experience in detection engineering, incident response, security engineering, or software engineering with a strong security focus
Strong software engineering skills with experience building production systems
Experience investigating security incidents in cloud-native or distributed environments
Familiarity with modern cloud infrastructure, Kubernetes, Linux, and networking
Experience building detections using logs, telemetry, behavioral signals, or large-scale event data
Strong SQL skills for investigating security events and developing detections
Interest in applying AI and LLMs to detection, investigation, and response, including understanding emerging threats involving AI-powered systems
Strong written and verbal communication skills
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
Job details are sourced from the employer's original posting.
Open job postingAbout the company
Modal provides the infrastructure foundation for AI teams. With instant GPU access, sub-second container startups, and native storage, Modal makes it simple to train models, run batch jobs, and serve low-latency inference. Companies like Suno, Lovable, and Substack rely on Modal to move from prototype to production without the burden of managing infrastructure.