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About This Role
TrendAI™, the global AI security leader and enterprise business unit of Trend Micro, empowers organizations with full AI visibility and consolidated security that inspires confidence, drives innovation, and eliminates risk.
At TrendAI™, we’re always seeking exceptional talent; people who want to collaborate with the best and push boundaries together. Here, your work goes beyond building a career. You will help protect what matters and play a vital role in shaping a safer, more trustworthy AI\-powered future.
AI Fearlessly.
We're hiring a new role in our People Operations team. This is a senior position responsible for designing, implementing, and optimizing the AI\-powered workflows, automations, and technology that support our employee lifecycle.
This is a non\-traditional individual contributor role. You'll be the person who looks at a manual process — onboarding, offboarding, benefits administration, compliance tracking, data reporting — and asks, “why is a human still doing this step by step?” You'll design the workflow and build or configure the automation (AI agents, integrations, HRIS logic). You'll partner closely with the team to eliminate manual work, improve efficiency, and build scalable HR processes that enhance the employee experience. While automation is your primary focus, you'll also roll up your sleeves and support day\-to\-day People Operations as business needs evolve, ensuring the solutions you build are practical, effective, and grounded in the employee experience.
The position reports to Director of People Ops and will work closely with the AI\-solutions teams.
What You'll Do
*Automate Relentlessly*
- Identify manual, repetitive, or error\-prone HR processes and redesign them for automation first, human\-in\-the\-loop second.
- Build and deploy AI\-powered workflows and agents for tasks such as onboarding, offboarding, document processing, employee inquiries, and compliance activities.
- Use automation platforms and AI tools to connect Workday, ATS, and other People systems into a seamless operating environment.
- Design scalable workflows that eliminate unnecessary manual work while ensuring the appropriate level of human review where judgment required.
- Maintain documentation and governance for every automated process, including triggers, business rules, exception handling, and escalation paths.
*Drive Process Improvement*
- Audit People Operations workflows to identify opportunities to improve efficiency, eliminate redundancy, and reduce compliance risk.
- Build reporting and dashboards that provide leadership with real\-time visibility into key employee lifecycle metrics, including onboarding completion, time\-to\-productivity, compliance status, and operational performance.
- Recommend and implement technology solutions that improve scalability, accuracy, and the employee experience.
Lead the Employee Lifecycle
- Own the operational delivery of onboarding and offboarding, ensuring employees have seamless experience from hire through separation.
- Partner with HR Business Partners, Talent Acquisition, Payroll, Benefits, and IT to execute employee lifecycle transactions accurately and efficiently.
- Ensure employee records, documentation, and HR processes remain accurate, compliant and audit ready.
- Continuously evaluate employee lifecycle processes to improve efficiency, consistency, and the employee experience.
Support People Operations
- Assist with HR operational activities during periods of high volume, organizational change, or critical business initiatives.
- Use firsthand operational experience to identify opportunities for automation, simplification, and continuous improvement.
- Serve as a trusted resource to the People Operations team by balancing strategic automation initiatives with hands\-on operational support.
What We're Looking For
- 5\+ years of experience in HR Operations, or a similar HR generalist function, ideally in a tech or high\-growth environment
- Experience managing employee lifecycle operations, including onboarding, offboarding and HR transactions in a fast\-paced environment.
- Comfortable balancing strategic automation projects with hands\-on operational support when priorities shift.
- A continuous improvement mindset with the ability to move seamlessly between designing future\-state solutions and executing day\-to\-day operations.
- Hands\-on experience with Workday
- Demonstrated experience building or implementing automation — whether through no\-code tools, AI agents/assistants, or scripting
- A track record of taking a manual HR process and rebuilding it to require less human touch, with real before/after results you can point to
- Strong working knowledge of US employment law and HR compliance fundamentals
- Excellent judgment on when human empathy and discretion matter
- High attention to detail and comfort handling confidential employee data
- A builder's mindset: you'd rather fix the process than work around it
- Prompt engineering or basic API/integration fluency
Why TrendAI
You will get to work inside a company that takes AI seriously for employees just as much as we do for our customers. You’ll have the support and space to innovate and collaborate across multiple teams and countries as you create the next generation of how Human Resources operates.
What We Offer You:
You're important to us. What matters to you, matters to us too. Trend Micro provides benefit options for you and your family.
- Comprehensive medical, dental and vision insurance
- Life insurance
- Short \& Long Term Disability
- Pre\-partum, maternity, parental and medical leave
- Mental Health Wellness Program
- Adoption Assistance
- Wellness Incentive
- Pet Insurance
- 401(k) with company match
- Paid Time Off
- 14 Annual Holidays
- Tuition Assistance
- Employee Resource Groups
We offer competitive compensation with bonus opportunity tied to company performance, along with room to enhance your skills through ongoing learning and broad technological opportunities. Achieving work\-life balance is a priority, complemented by team activities, fostering an environment rooted in equity, inclusion, and collaboration, that is reflected in both our culture and our work.
*Be Passionate. Be Innovative. Be a Trender.*
This position does not offer sponsorship for work permit applications or renewals, either now or in the future. Candidates must be authorized to work in the U.S. without the need for employment\-based visa sponsorship, both currently and moving forward. The company will not sponsor applicants for U.S. work visa status for this role (including, but not limited to, H\-1B, L\-1, TN, O\-1, E\-3, H\-1B1, F\-1, J\-1, OPT, CPT, or any other employment\-based visa).
\#LI\-LO1
At Trend Micro, we embrace change, empower people, and encourage innovation in a connected world. Our diversity and multicultural workforce are key contributing factors to our success across the globe. Trend Micro provides equal employment opportunity for all applicants and employees. Trend Micro does not unlawfully discriminate on the basis of race, color, religion, sex, pregnancy and childbirth or related medical conditions, national origin, ancestry, age, physical or mental disability, medical condition, family care leave status, veteran status, marital status, sexual orientation, or gender identity.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Trend Micro Inc., 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Trend Micro Inc. AI Hiring
Trend Micro Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Irving, TX, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. 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: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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