Software Development Engineer (Machine Learning)

FortiAIGate is Fortinet's 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 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.
• 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 $150,000-$183,000.
• 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
• candidate's 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 Company’s
• 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
• 890,000+ customers around the globe.
• Fortinet (NASDAQ: FTNT) secures the largest enterprise, service provider, and
• government organizations around the world. Fortinet empowers its customers with
• intelligent, seamless protection across the expanding attack surface and the
• power to take on ever-increasing performance requirements of the borderless
• network - today and into the future. Only the Fortinet Security Fabric
• architecture can deliver security without compromise to address the most
• critical security challenges, whether in networked, application, cloud or mobile
• environments. Fortinet ranks number one in the most security appliances shipped
• worldwide and more than 500,000 customers trust Fortinet to protect their
• businesses.
• We are committed to providing reasonable accommodations for all qualified
• individuals with disabilities. If you require assistance or accommodation due to
• a disability, please contact us at [email protected].
• Fortinet is an equal opportunity employer. We value diversity in our company,
• and all qualified applicants will receive consideration for employment without
• regard to race, color, religion, sex, sexual orientation, gender identity,
• national origin, disability, age, military/veteran status or any other
• applicable legally protected characteristics in the location in which the
• candidate is applying.