Software Development Engineer (Machine Learning)

FortiAIGate is Fortinets AI security and governance gateway. It sits inline between enterprise users AI agents and LLM providers inspecting prompts and responses in real time to detect prompt injection jailbreaks sensitive data exposure and policy violations under a strict latency budget.

We are hiring a Machine Learning Engineer to own the detection models behind that product: training evaluation optimization and the serving stack that runs them in production.

Responsibilities

• Build and train guardrail 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 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 -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 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 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 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.

Required Qualifications

• Strong Python and production PyTorch experience; comfort with Go/Rust/C/C for performance-critical paths is valuable.
• Demonstrated experience training fine-tuning and evaluating transformer models encoder classifiers decoder language models or both with Hugging Face Transformers or equivalent.
• Production experience with a modern inference serving system (Triton vLLM TensorRT-LLM TGI) including the batching and memory tuning real throughput requires.
• Practical model optimization: quantization distillation pruning or graph compilation with a record of holding accuracy while cutting latency or memory.
• Sound evaluation instincts able to design test sets that reflect deployment reality and reason about precision/recall where false positives block legitimate user traffic.
• Working knowledge of tokenization text normalization and Unicode handling and how these become an attack surface in a security product.
• Familiarity with containerized deployment (Docker Kubernetes) and standard MLOps practice: experiment tracking model versioning reproducible training pipelines.
• Ability to deliver on schedule in an Agile environment and communicate effectively across technical and non-technical teams.

Preferred Qualifications

• Modeling experience in a security or abuse-detection domain where adversaries adapt to your defenses.
• Familiarity with the LLM threat landscape prompt injection indirect injection exfiltration through model output and with the OWASP Top 10 for LLM Applications.
• Gradient-boosted tree models (LightGBM XGBoost) and hybrid classical/neural architectures.
• NER PII detection or data classification models particularly multilingual.
• CUDA familiarity GPU profiling or deploying models under fixed hardware and memory constraints.
• Synthetic data generation active learning or human-in-the-loop labeling where labeled data is scarce.
• Publications open-source work or CTF/red-team experience in adversarial ML or LLM security.

Must be authorized to work in the U.S. without sponsorship.

The US base salary range for this full-time position is $150000-$183000. Fortinet offers employees a variety of benefits including medical dental vision life and disability insurance 401(k) 11 paid holidays vacation time and sick time as well as a comprehensive leave program.

Wage ranges are based on various factors including the labour market job type and job level. Exact salary offers will be determined by factors such as the candidates subject knowledge skill level qualifications experience and geographic location.

All roles are eligible to participate in the Fortinet equity program. Bonus eligibility is reviewed at the time of hire and annually at the Companys discretion.

Why Join Us:

We encourage candidates from all backgrounds and identities to apply. We offer a supportive work environment and a competitive Total Rewards package to support you with your overall health and financial well-being.

Embark on a challenging enjoyable and rewarding career journey with Fortinet. Join us in bringing solutions that make a meaningful and lasting impact to our 890000 customers around the globe.