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About Gurucul
Security teams are dealing with change from both directions. Attackers use AI to automate reconnaissance and exploitation, while AI agents inside companies introduce new risks through their access, decisions, and actions. At the same time, AI is changing how security teams work. Organizations need to decide which tools to adopt, what they can trust, and how much autonomy to allow.
SOC teams count on Gurucul to help them navigate these changes. We build the SIEM and the AI that works in it. Founded in 2010, Gurucul helped pioneer user and entity behavior analytics (UEBA) and has since built AI agents into data handling, detection, triage, investigation, and response. We’re a Leader in the 2025 Gartner® Magic Quadrant™ for SIEM. We’re dedicated to helping security teams handle the threats in front of them and prepare for what comes next.
Role Summary
The Director of Product Marketing will be central to that work. This is a player-coach role on a small, scrappy team, starting with one direct report and plans to grow. You’ll lead the product marketing function while doing the work yourself. We want someone who recognizes which market shifts matter and judges the work by its contribution to pipeline, wins, and adoption. You can get deep into technical details and turn that understanding into clear, customer-centered messaging and stories. You’re comfortable making decisions with incomplete information and building a function as the business grows. You might be a strong individual contributor ready to lead, or an experienced manager who still enjoys writing, launching, and working with the field.
We value customer-centricity, experimentation, clarity, collaboration, and a focus on results. We respect people’s points of view and expect them to change their minds when the evidence calls for it. If you like learning a product deeply, telling its story, and helping the people around you get better, we’d like to meet you.
Key Responsibilities
- Market and category strategy. Help define where Gurucul competes and lead how we position our products with buyers and analysts. Spend time with customers, prospects, and practitioners to understand who buys, who evaluates, and what changes a decision. Lead win-loss analysis and competitive intelligence, examine customer and marketing data, and bring what you learn into messaging, go-to-market plans, and product strategy.
- The story. Build a compelling narrative that connects a buyer’s problem to why Gurucul is the right choice. Answer questions about trust, explainability, and control with specific capabilities and evidence. Turn the narrative into presentations, web copy, demo stories, and executive talk tracks that buyers remember and sellers can deliver with confidence.
- Proof. Know the product well enough to explain how it works and where its limits are. Build strong relationships with Product Management and Sales Engineering to validate claims, develop scenarios, and capture customer outcomes. Clearly distinguish released capabilities, previews, and future direction in every story.
- Launches and go-to-market. Define the audience, use cases, offer, launch plan, and sales enablement for major releases, and carry those plans through execution. Work with Product Management on packaging and pricing, and with Marketing on campaigns. Develop distinct plans for acquiring new customers, increasing adoption, and expanding existing accounts, each with clear success measures.
- Sales enablement. Create pitch decks, competitive guidance, demo narratives, and sales plays that help move real deals forward. Equip channel partners and MSSPs with a consistent product story. Work with sellers and SEs on discovery, evaluations, and objections, and use what you hear to improve our positioning and content.
- Customer reach. Create thought leadership, customer stories, and launch content that give buyers a reason to pay attention. Lead the product narrative in analyst briefings and represent Gurucul in customer meetings, webinars, and events.
- The team and its impact. Set priorities for the function, recruit product marketers, and help them grow. Measure whether the work helps buyers choose Gurucul through field adoption of key assets, pipeline contribution, competitive wins, and product adoption. Use those findings to decide what to improve, continue, or stop.
Required Qualification
- 8+ years in enterprise B2B product marketing or closely related roles, with substantial cybersecurity experience and ownership of positioning, launches, and sales enablement.
- You go deep on technology and competitors. You understand how products work, how they compare, and which trends matter beyond the headlines.
- You start with the customer and work backward. You learn what buyers need through sales conversations, customer interviews, and win-loss analysis, and that understanding shapes your work.
- You make strategic decisions and back them with data. You can assess an opportunity, choose which segments and competitors to focus on, explain the tradeoffs, and use pipeline, win-rate, and adoption data to evaluate results.
- You’ve strengthened or changed how buyers see a product or category, whether by defining a new category, repositioning a product, or differentiating against established competitors. You can explain what you changed and what happened next.
- You understand the security market and the business behind the message: how enterprise security teams evaluate and buy, who the incumbents are, and how pricing and packaging affect a deal.
- Experience marketing AI capabilities to security or IT buyers, with a practical understanding of agents, LLMs, and buyer questions about data access, reliability, and human oversight.
- You write and present exceptionally well, in a voice security buyers trust. You can show work that made a complex product, technology, or idea easier to understand and helped a seller or customer make a decision.
- You’ve earned the trust of product and sales leaders and can influence a change in direction when the evidence supports it.
- A track record of coordinating cross-functional teams, resolving competing priorities, and delivering a shared result.
- Experience mentoring, coaching, or managing product marketers, with good judgment about what a small team should prioritize. You use AI tools to accelerate research and production while taking responsibility for the accuracy and quality of what ships.
Preferred Qualifications (Nice-to-Have)
- Formal people management experience, including hiring and developing product marketers.
- Knowledge of security for AI, including how LLMs, agent harnesses, and agentic frameworks affect behavior, access, and risk.
- Experience leading analyst briefings, influencing how industry analysts cover a category, and presenting in executive meetings, webinars, or events.
- Experience positioning against established SIEM incumbents and developing migration or displacement plays.
Compensation & Benefits
- Annual base salary range: $185,000 – $220,000
- Gurucul offers a competitive compensation package, including medical, dental, vision, and life insurance, EAP, 401K, Paid Time Off, and paid Holidays
Location
- Hybrid in El Segundo, CA, or remote within the US.
Equal Opportunity Employer
Gurucul Solutions, LLC is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
To apply:
Please send resumes to [email protected] for consideration.
Salary Context
This $185K-$220K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Gurucul, 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 in Demand for This Role
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 $211,950 based on 13,048 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300. Disclosed range: $185K to $220K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Gurucul AI Hiring
Gurucul has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in El Segundo, CA, US. Compensation range: $210K - $220K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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