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.
About the job
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
- Experience building security automation pipelines
- Experience securing multi tenant infrastructure
Skills
Kubernetes, AWS, GCP, Azure, Python, Go, PyTorch, TensorFlow, Hugging Face, CI/CD
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