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About This Role
About the Position
WatchGuard is looking for a Director of AI Product Management to own the strategy and execution for Rai, our agentic, AI\-powered action layer built on WatchGuard Cloud, WatchGuard’s MSP focused security platform. This is a high\-impact role at the intersection of agentic AI, cybersecurity, and the managed services market.
Rai handles the MSP workforce jobs that are structured, repeatable, and grounded in data that already lives in WatchGuard Cloud. It doesn’t just surface information; it closes loops. This PM owns what those loops look like, how reliable they are, and how MSPs learn to trust and extend them.
Reporting to the Chief Product Officer, this individual will be responsible for driving the Rai product roadmap, working cross\-functionally with engineering, design, channel, and go\-to\-market teams, and ensuring that WatchGuard’s AI platform delivers measurable value to MSP partners.
A Day in the Life
As a Director of AI on the Platform team, you will work at the center of one of WatchGuard’s most strategically important investments. You will spend your time in direct conversation with MSP partners, understanding how they staff their SOCs and NOCs, where their technicians lose time, and what it would mean to their business to have those hours back. You will translate that understanding into agentic workflows that Rai can own autonomously, and work with engineering to define how those workflows behave when data is incomplete, confidence is low, or actions cannot be undone. AI tools are a native part of how you work; you use them to synthesize customer research, accelerate discovery, apply Spec Driven Design principles to structure requirements before engineering picks them up, and validate prototypes faster than traditional methods allow. You evaluate AI feature quality not just by adoption but by accuracy, reliability, and the degree to which MSPs choose to expand Rai’s scope over time. You drive roadmap alignment across a cross\-functional team using working prototypes and real partner feedback, and you partner with PMM and the channel to ensure that what gets built also gets understood and sold.
### Position Responsibilities
- Business ownership: Ensure the agents are monetizable and a commercial success. You will drive the ideation, design, and development of AI\-powered agents and solutions, with a focus on creating monetizable agentic capabilities aligned to MSP market needs.
- Roadmap ownership: Own the Rai product roadmap from discovery through delivery, balancing near\-term partner value with the longer\-term platform convergence vision.
- Agentic workflow definition: Define and prioritize agentic workflows that move MSPs from visibility to decision to action, replacing or extending MSP workforce jobs rather than just adding a chat interface.
- AI evaluation and quality: Establish evaluation frameworks for AI features, including how WatchGuard defines quality bars, measures accuracy and reliability, and decides when an automated action is ready for production.
- Customer discovery: Work directly with MSP partners to understand workflows and pain points, and validate product direction through direct customer engagement.
- Cross\-functional alignment: Drive alignment across engineering, channel, PMM, and leadership using working prototypes and real partner feedback. Clearly conveying the outcomes to each stakeholder.
- AI safety and reliability: Own AI safety and reliability as a product responsibility, including how Rai behaves when confidence is low, when actions are irreversible, and when the MSP’s trust is on the line.
- Go\-to\-market partnership: Partner with PMM, channel, and sales to translate product capability into GTM strategy, partner messaging, and enablement.
- Performance monitoring: Define and track success metrics for Rai features: automation rate, ticket deflection, time\-to\-action, accuracy, and downstream business outcomes for MSPs.
- Competitive intelligence: Monitor the AI and MSP platform competitive landscape to identify differentiation opportunities.
### Required Qualifications
- MSP market knowledge: Deep familiarity with how managed service providers operate, including how they structure their teams, price and deliver services, manage margin pressures, and where technician time goes. You understand that for MSPs, simplicity and automation are not features; they are the business case. You know the difference between a tool an MSP will actually adopt and one that adds process to an already stretched team.
- Agentic AI product experience: Demonstrated depth in product management, with meaningful hands\-on experience shipping agentic AI or LLM\-powered automation in a B2B context — typically 8\+ years overall and at least 2 years working directly on autonomous or semi\-autonomous AI workflows where the system takes action on behalf of the user. Candidates who have shipped real agentic products recently will be weighted over those with tenure alone.
- LLM technical fluency: Hands\-on familiarity with how LLMs work in production: context limits, latency tradeoffs, hallucination risks, and when RAG, fine\-tuning, or deterministic fallbacks are the right answer.
- AI evaluation and governance: Experience defining evaluation criteria and quality standards for AI actions, including how to validate that an automated workflow is safe to run unsupervised.
- AI\-native product approach: AI tools are part of your core workflow. You use them for customer research synthesis, Spec Driven Design, and prototype validation, getting to a well\-structured spec faster and with more rigor than traditional methods allow. You think in terms of what AI can own end\-to\-end, not just where it can assist.
- Product instincts: Strong instincts for what makes an agentic feature genuinely useful versus impressive in a demo, especially in an MSP context where trust, reliability, and low\-friction adoption determine whether a product survives the first 90 days.
- Cross\-functional collaboration: Comfort working across engineering, design, and go\-to\-market in a fast\-moving environment.
- Communication skills: Clear, direct communicator who can move between technical depth and business narrative depending on the audience.
### Nice to Have
- PSA and RMM familiarity: Experience with MSP operational tooling including ConnectWise, Autotask, NinjaOne, or HaloPSA.
- Security or platform background: Background in cybersecurity products, managed services, or multi\-tenant SaaS platforms.
- Prototyping experience: Experience building or evaluating functional prototypes as a discovery and alignment tool.
- Responsible AI: Understanding of responsible AI in production environments, including explainability, auditability, rollback behavior, and least\-privilege action design.
Compensation
The base salary range is $225,000\-$245,000 per year for full\-time employment, exclusive of benefits. This position is also eligible for a bonus of 20% of the base salary. Your base salary will be determined by your individual skills, education, and experience. Hiring at the maximum of the range is not typical in order to allow for future salary growth.
U.S. Benefits
- Comprehensive benefits plan including medical, dental, vision, disability, and life insurance
- Healthcare HSA
- HSA with employer contribution
- 10 paid holidays
- 10 days of paid annual leave
- 9 days of paid sick time
- Paid parental leave
- 401(k) with employer match
Other Perks
- Education assistance program
- Dependent Care HSA match
- Adoption assistance
- Fertility care support
- Backup care for family and pets
- A growing network of employee resource groups
- Employee referral program
- Employee Assistance Program
If this role sounds like the work you’ve been building toward, we’d like to hear from you. Submit your resume and a brief note on what drew you to this problem space. Cover letters are optional — tell us what matters to you, not what you think we want to hear.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $225K-$245K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Watchguard Technologies, 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800. This role's midpoint ($235K) sits 30% above the category median. Disclosed range: $225K to $245K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Watchguard Technologies AI Hiring
Watchguard Technologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Seattle, WA, US. Compensation range: $245K - $245K.
Location Context
AI roles in Seattle pay a median of $227,400 across 1,084 tracked positions. That's 14% above the national 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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