Senior Application Security Engineer, AI and Machine Learning
Secures AI/ML systems through threat modeling, architecture reviews, and securing inference pipelines, APIs, and model supply chains. Partners with ML engineers to embed security in training, deployment, and multi-tenant workloads while building automation tooling.
180k – 220k/yr
HybridSecurity Engineering
About the role
What You’ll Do
Secure AI and Machine Learning Systems
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
Application Security Engineering
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
Inference Platform Security
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
AI Supply Chain and Model Security
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
Security Automation and Tooling
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
What You'll Need
Required Experience
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
AI and Machine Learning Experience
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
It's a Strong Plus If You Have
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
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