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About This Role
About the Role
We are looking for a Junior Applied AI Engineer\- HRIS to join our HRIS team and help build intelligent, data\-driven solutions that enhance HR processes and employee experiences. This role will report to the Director of HRIS at Fortinet, and will work at the intersection of artificial intelligence, data engineering, and HR technology, contributing to the development of AI\-powered tools such as AI Agents, chatbots, analytics models, and workflow automation systems.
This is an excellent opportunity for someone early in their career who is passionate about applying AI in real\-world business contexts, particularly in HR systems.
Key Responsibilities
- Assist in designing, developing, and deploying AI/ML models to support HR functions (e.g., recruiting, onboarding, employee engagement, retention).
- Collaborate with HR, IT, and data teams to understand business needs and translate them into technical solutions.
- Build and maintain data pipelines for HR data (structured and unstructured).
- Support development of AI\-powered features such as Agents, chatbots, recommendation systems, and predictive analytics.
- Document technical processes, models, and workflows.
- Demonstrate ability to work with sensitive data while maintaining confidentiality and sound judgment.
Required Qualifications
- Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, or a related field.
- 0–2 years of experience in AI/ML, data science, or software engineering
- Understanding of what LLMs are and how they work at a high level (tokens, prompts, context window)
- Awareness of common issues (hallucinations, bias, context limits)
- Understanding of machine learning concepts and algorithms
- Proficiency in Python and common AI/ML libraries (e.g., TensorFlow, PyTorch, scikit\-learn)
- Experience with Database (SQL/ No SQL)
- Understanding of REST APIs and basic software development practices
- Strong problem\-solving and analytical skills
- Good communication and teamwork abilities
Preferred Qualifications
- Exposure to Natural Language Processing (NL)P and conversational AI
- Experience working with cloud platforms (e.g., OCI, AWS, Azure, or GCP)
- Familiarity with SaaS based HR platforms (e.g., Oracle, Workday, etc.)
- Experience with visualization tools (e.g., Tableau, Power BI)
- Knowledge of version control systems like Git
- Experience building chatbots or automation tools
What You’ll Gain
- Hands\-on experience applying AI in a business\-critical domain.
- Exposure to HR technology ecosystems and enterprise systems.
- Opportunity to work on impactful, user\-facing AI solutions.
Key Competencies
- Curiosity and willingness to learn
- Attention to detail
- Adaptability in a fast\-paced environment
- Collaborative mindset
- Interest in AI applications in HR and business processes
Other
- Must be authorized to work in the U.S. without sponsorship
The US base salary range for this full\-time position is $105,000\-$140,000\. 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.
Salary Context
This $105K-$140K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Fortinet, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $166,983 based on 13,781 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $76,880. This role's midpoint ($122K) sits 27% below the category median. Disclosed range: $105K to $140K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Fortinet AI Hiring
Fortinet has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer. Based in Sunnyvale, CA, US. Compensation range: $140K - $160K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (2,416) are outnumbered by mid-level (16,247) and senior (5,153) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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