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About Gurucul
As the threat of autonomous attacks and rogue agents grows, security teams are rushing to adapt. Right now they are deciding how much work to hand to AI, and what it takes to trust an AI’s conclusion before it acts. Gurucul has spent more than a decade on the groundwork for that trust: learning how users, systems, and now AI agents normally behave, and showing the evidence when behavior changes. We were early to market with an AI SOC solution that supports analysts at every stage of the lifecycle. We helped pioneer user and entity behavior analytics (UEBA), and we’re a Leader in the 2025 Gartner® Magic Quadrant™ for SIEM. This is all unified by a platform that connects SIEM, Insider Risk Management, and AI Risk and Response, with AI agents embedded into the workflows.
Role Summary
You’ll own the AI platform under every Gurucul product: the models, agents, and evidence that SIEM, Insider Risk Management, and AI Risk and Response build on, and every way people and other systems reach them. Some customers will work in their own AI Consoles. Others will want Gurucul’s detections, investigations, and actions inside the AI tools and workflows they already run, through APIs and MCP. You’ll decide where we invest and take this through execution and deployment, working closely with our customers. This is the biggest area for innovation in security and you’ll be at the center of it. You’ll work closely with colleagues across multiple time zones, requiring flexibility in working hours and comfort operating in a distributed organization.
In this role, you’ll be both a hands-on product leader and an organizational leader, initially driving the platform directly and, over time, managing and coaching product managers and product owners. You’ll report to our VP of Product Management and work across Engineering, Research, UX, and the product teams. This role suits someone who has led a platform through a major change in how customers used it, and who can make hard calls before the market settles. Those calls will shape Gurucul for years, and you’ll explain them to customers, analysts, and industry audiences.
Key Responsibilities:
Platform strategy and business ownership
- Set AI platform strategy across SIEM, Insider Risk Management, and AI Risk and Response. Decide which capabilities belong in the shared platform and which belong to one product, and make investment calls on customer value, differentiation, effort, and risk.
- Define how Behavioral AI, deterministic detections, Unified Entity Intelligence, LLMs, and agents work together. Set priorities for prediction and prevention, and be explicit about what the platform can infer, what it can stop, and when a person must approve the action.
- Own the business case. Work with Product Marketing, Sales, and Finance on positioning, pricing, packaging, and launches, including how to price AI capabilities used through APIs and agents. Track adoption, customer outcomes, revenue contribution, and the cost of running AI features.
Interfaces and ecosystem
- Own the AI Console with UX and Engineering: how people ask questions, inspect evidence, direct agents, approve actions, and recover from mistakes. Measure whether people finish the work with less effort.
- Set direction for APIs and Model Context Protocol (MCP) integrations, covering both Gurucul agents that call external tools and external AI systems that call Gurucul. Define use cases, permissions, tenant boundaries, approval rules, and audit trails so a capability behaves the same however it’s invoked. Decide where to partner with model providers and AI platforms and where to build.
Trustworthy AI and discovery
- Make grounding and guardrails product requirements: evidence behind every conclusion, access controls, data privacy, tool permissions, human approval, audit trails, and safe failure handling. Work with Engineering and Research on prompt injection, data leakage, and unintended actions.
- Set evaluation standards with Research and Engineering before release and monitor performance after. Track task completion, evidence quality, wrong conclusions or actions, analyst overrides, latency, and cost. Require regression checks as models, prompts, and tools change, and stop capabilities that don’t hold up.
- Lead discovery with SOC and insider risk practitioners and design partners. Use prototypes and pilots to test usefulness, feasibility, willingness to pay, and whether customers trust the results enough to rely on them, before committing major investment.
Leadership and influence
- Hire, manage, and coach product managers. Lead initiatives across teams outside your reporting line: get agreement on priorities, owners, and success measures, raise risks early, and settle disagreements with evidence. Engineering owns architecture and implementation; you own the problem, the priorities, and the acceptance criteria.
- Represent Gurucul with customers, analysts, and conference audiences. Demo the platform, explain where our AI is headed and what it can’t do yet, and work with Product Marketing on a story the product can back up.
Required Qualifications
- 10+ years of product management or product leadership in enterprise software, including ownership of strategy and delivery across multiple teams and experience managing product managers.
- Experience operating in a fast-growing environment with startup-like constraints, balancing strategic investments, customer commitments, and engineering capacity.
- You’ve shipped AI products into production, including LLM or agent capabilities. You understand retrieval and grounding, tool use, evaluation, model selection, and the tradeoffs among quality, latency, and cost.
- You’ve owned a platform or API product that other teams, partners, or customers built on. You’re fluent in integrations, data platforms, and enterprise SaaS, and can write requirements for MCP-based integrations and work through tradeoffs with Engineering.
- You know security operations well enough to see what a wrong AI conclusion or action costs, and you can work with specialists on detection, investigation, and response.
- You’ve used research, prototypes, and pilots to change a roadmap, and you can tie product decisions to adoption, customer outcomes, pricing, or revenue.
- You communicate clearly with practitioners, engineers, executives, and public audiences, and you’ve won support for a direction before it was obvious.
Preferred Qualifications (Nice-to-Have)
- Experience building security agents, predictive analytics, or prevention and response capabilities with customer-defined controls.
- Experience with MCP servers, agent orchestration, or AI consoles, including production evaluation and enterprise access controls.
- Experience combining behavioral models, deterministic logic, knowledge graphs, and generative AI in one product.
- Experience leading platform products at a growing enterprise software company.
Compensation & Benefits
- Annual base salary range: $180,000 – $210,000
- 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 $180K-$210K 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. This role's midpoint ($195K) sits 8% below the category median. Disclosed range: $180K to $210K.
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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