Summary
What you’ll impact
Our organization is seeking a Senior Application Security Engineer to embed security into its AI, machine learning, and inference platforms. The role involves hands‑on technical work, threat modeling, security reviews, and building automation tools while collaborating with engineering and leadership teams.
Responsibilities
What you'll do
- Perform threat modeling across AI platforms, inference services, and ML pipelines
- Identify risks such as prompt injection, model extraction, adversarial inputs, and data leakage
- Review model serving architectures and inference pipelines
- Partner with ML engineers to secure training, fine tuning, and deployment workflows
- Help design isolation and security controls for multi tenant AI workloads
- Perform architecture and design security reviews
- Conduct targeted code reviews for high risk components
- Identify security gaps in APIs, micro-services, and distributed systems
- Build secure patterns for authentication, authorization, and service to service communication
- Help engineering teams implement secure defaults and guardrails
- Secure customer facing inference APIs and services
- Protect against abuse, model extraction, and adversarial behavior
- Design rate limiting, isolation, and workload protection controls
- Build monitoring and detection for anomalous inference behavior
- Evaluate open source models and dependencies
- Secure model artifacts and distribution pipelines
- Implement integrity validation and provenance controls
- Help secure container images and runtime environments
- Build security automation for AI and application pipelines
- Integrate security scanning into CI/CD workflows
- Develop tooling to help engineers detect and fix issues early
- Improve developer experience with security guardrails
Requirements
What you’ll bring
- Strong background in application security engineering
- Experience performing threat modeling and architecture reviews
- Experience securing APIs and distributed systems
- Experience working in cloud environments such as AWS, GCP, or Azure
- Experience with containers and Kubernetes
- Strong scripting or programming skills such as Python, Go, or similar
- Experience working closely with engineering teams to implement security improvements
- Experience securing ML pipelines, inference systems, or data platforms
- Familiarity with risks such as prompt injection, model extraction, and adversarial inputs
- Experience reviewing model serving architectures
- Understanding of training data security and data leakage risks
- Red team or offensive security experience
- Experience crafting payloads and evaluating CVEs for exploitability in diverse environments
- Experience with GPU infrastructure or high performance computing
- Experience with Hugging Face, PyTorch, TensorFlow, or similar frameworks
- Experience with LLM systems, RAG pipelines, or agent frameworks
- Experience building security automation pipelines
- Experience securing multi tenant infrastructure