Fortinet provides cybersecurity solutions for enterprise, service provider, and government organizations. The Machine Learning Engineer will own detection models for the FortiAIGate AI security and governance gateway, including model training, evaluation, optimization, and production serving. The role focuses on detecting prompt injection, jailbreaks, sensitive data exposure, and policy violations under strict latency requirements.
Responsibilities
- Build and train guardrail models. Develop classifiers that detect prompt injection, jailbreak attempts, unsafe content, and sensitive data exposure across prompts, responses, and tool-call payloads — dataset construction through to release
- Design and tune the tiered detection cascade. Balance a low-cost first-stage screen against a higher-fidelity semantic stage, tuning thresholds to hit accuracy targets inside a fixed per-request latency budget
- Work across encoder and decoder model families. Fine-tune encoder-based classifiers and token-level taggers for detection and extraction; adapt small decoder models for semantic judgment. Use distillation to move capability into models small enough to deploy
- Optimize and serve models inline. Quantize, distill, and compile models (ONNX Runtime, TensorRT, INT8/FP8) for GPU appliances. Deploy and tune them on Triton Inference Server and vLLM — batching, concurrent model execution, KV-cache and memory configuration, multi-stage pipelines — and profile out the bottlenecks
- Harden models against evasion. Threat research on obfuscation and encoding bypass, dilution attacks, indirect injection, and multi-turn attacks visible only across conversational context. Turn each new bypass into training data and a regression test
- Own evaluation and governance detectors. Build benchmark and suites measuring detection rate at production-realistic false positive rates; monitor deployed models for drift. Maintain detection models for personal and regulated data and for natural-language policy, including multilingual coverage