Applied AI Engineer - AI Agent (PhD New Graduate)

We are a cybersecurity company building a next-generation AI-driven operations platform, designed to power complex, high-stakes workflows. Our product integrates generative AI deeply into real-time operational environments-combining reasoning, retrieval, and automation into scalable, trustworthy systems.

The Opportunity

We're seeking PhD new graduates who bring deep research expertise in AI/ML and are eager to translate that knowledge into production-grade systems. As an Applied AI Engineer, you will bridge the gap between cutting-edge research and real-world product delivery. Your doctoral-level understanding of machine learning, NLP, multi-agent systems, or related fields will directly inform the architecture and capabilities of our platform. You'll partner with product, engineering, and design teams to drive investigation speed, streamline security operations, and pioneer new forms of human-AI collaboration.

What You'll Do

Research-to-Production Development

* Architect and implement scalable AI agent and backend systems for high-volume, real-time operational workloads, informed by state-of-the-art research.

* Design and integrate advanced GenAI components into production workflows-including fine-tuning strategies, multi-step reasoning chains, retrieval-augmented generation pipelines, and systematic evaluation frameworks.

* Develop novel approaches to data flow architecture (event streams, message queues, job orchestration) for next-generation SOC/NOC capabilities.

* Define clear contracts and abstractions between AI services, backend APIs, and frontend clients that scale with model and system complexity.

* Drive trustworthy AI delivery: streaming responses with structured outputs, robust guardrails, redaction mechanisms, and human-in-the-loop review systems.

Technical Leadership & Collaboration

* Serve as a technical thought partner on AI system design, helping the team navigate trade-offs between model performance, latency, cost, and safety.

* Partner with backend engineers to build data pipelines and infrastructure that support advanced AI capabilities.

* Collaborate with design and frontend engineers to translate complex AI behaviors into intuitive, explainable user experiences.

* Lead technical design reviews, contribute to architectural decision records, and help shape engineering best practices.

* Communicate research insights and system trade-offs to both technical and non-technical stakeholders with clarity and precision.

Who You Are

Education

* PhD in Computer Science, Machine Learning, Natural Language Processing, Information Retrieval, Cybersecurity, or a closely related field (graduated within the past 6 months or defending by Summer 2026).

Technical Skills

AI/ML Expertise (Primary)

* Deep expertise in one or more areas: large language models, NLP, multi-agent systems, reinforcement learning, information retrieval, or AI safety.

* Hands-on experience with model training, fine-tuning, and evaluation at scale (e.g., PyTorch, JAX, Hugging Face Transformers).

* Strong understanding of retrieval-augmented generation (RAG), prompt engineering, chain-of-thought reasoning, and agentic AI patterns.

* Familiarity with evaluation methodologies for generative AI systems (automated metrics, human evaluation, red-teaming).

* Experience designing experiments, analyzing results rigorously, and iterating on model and system performance.

Backend & Systems Engineering

* Proficiency in Python (required) and ideally a second systems language (Go, Rust, or C++).

* Experience with API design (REST, WebSocket, or GraphQL) and integration with real-time or event-driven architectures.

* Familiarity with databases and storage paradigms relevant to AI workloads (vector stores, graph DBs, time-series databases, Postgres).

* Understanding of distributed systems, scalability patterns, and observability in production environments.

* Exposure to MLOps practices: model versioning, deployment pipelines, monitoring, and A/B testing.

Communication & Soft Skills

* Ability to distill complex research concepts into actionable engineering plans.

* Excellent written communication, demonstrated through publications, technical reports, or documentation.

* Comfortable operating in fast-moving, ambiguous product environments where research meets production constraints.

* Experience mentoring junior researchers or engineers (teaching assistantships, lab mentorship, or similar).

What Sets You Apart

* Published papers at top-tier venues (NeurIPS, ICML, ACL, EMNLP, USENIX Security, CCS, IEEE S&P, or equivalent).

* Experience building and deploying ML/AI systems beyond research prototypes (industry internships, open-source projects, or startup experience).

* Research contributions to AI safety, alignment, interpretability, or robustness of generative models.

* Contributions to widely-used open-source AI/ML libraries or frameworks.

* Domain expertise in cybersecurity, threat detection, or security operations.

What We Offer

* A unique opportunity to apply doctoral research to real-world cybersecurity challenges at enterprise scale.

* End-to-end ownership of high-impact AI capabilities used daily by security teams worldwide.

* Collaborative environment with experienced engineers, applied researchers, and designers who value research rigor.

* Freedom to publish and present at conferences, maintaining your connection to the research community.

* Continuous learning across AI/ML, distributed systems, and modern platform engineering.

* Comprehensive benefits including medical, dental, vision, life and disability insurance, 401(k), 11 paid holidays, vacation time, and sick time.

Compensation

The US base salary range for this full-time position is $179,500-$260,000. Exact salary offers will be determined by factors such as research focus, publication record, skill level, qualifications, 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.