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About This Role
About Delinea:
Delinea is a pioneer in securing human and machine identities through intelligent, centralized authorization, empowering organizations to seamlessly govern their interactions across the modern enterprise. Leveraging AI\-powered intelligence, Delinea’s leading cloud\-native Identity Security Platform applies context throughout the entire identity lifecycle – across cloud and traditional infrastructure, data, SaaS applications, and AI. It is the only platform that enables you to discover all identities – including workforce, IT administrator, developers, and machines – assign appropriate access levels, detect irregularities, and respond to threats in real\-time. With deployment in weeks, not months, 90% fewer resources to manage than the nearest competitor, and a 99\.995% uptime, Delinea delivers robust security and operational efficiency without compromise. Learn more about Delinea on Delinea.com, LinkedIn, X, and YouTube.
Join our passionate, global team at Delinea and help us make the world a safer and more secure place. Our success is driven by world\-class product leadership, outstanding engineers, and strategic investment from TPG. We value diversity, innovation, and a culture of respect and fairness. If you're ready to push boundaries and challenge the status quo in security, we want to hear from you.
Apply today to help us achieve our mission.
Position Summary:
The AI Marketing Solutions Engineer is responsible for executing AI\-powered marketing programs that improve productivity, accelerate campaign delivery, and enhance customer engagement across the marketing organization. Working closely with demand generation, content, digital, solution marketing, operations, and creative teams, this role identifies opportunities to apply AI across marketing workflows and turns them into scalable, repeatable processes.
The role combines marketing execution with hands\-on AI expertise, including prompt engineering, workflow design, AI content creation, automation, and optimization. The AI Marketing Solutions Engineer will write and refine prompts for large language models (LLMs), develop reusable prompt libraries, test emerging AI tools, and embed AI into day\-to\-day marketing operations to improve quality, speed, and performance.
Success in this role requires a blend of strategic thinking and practical execution, with a focus on driving measurable business outcomes while ensuring AI is used responsibly, consistently, and effectively across the marketing team.
What You'll Do:
- Design and build custom Marketing AI skills, workflows and agents that help automate marketing tasks, campaign development, draft content production and refinement, research, and reporting.
- Build and execute skills and workflows to automate system processes and tasks within integrated marketing campaigns, with human\-in\-the\-loop input/guardrails across systems and channels, from concept through launch and results.
- Package skills within projects and automated workflows to help streamline and compound growth and use.
- Partner with Demand Generation, Content, Solutions Marketing, Brand, Creative, and Marketing Operations to embed AI into marketing programs and day\-to\-day workflows.
- Prototype and pilot AI tools, skills, and agents across platforms, including Claude, ChatGPT, custom GPTs and automation platforms, and recommend what scales.
- Evaluate emerging AI tools and technologies, lead pilot programs, and recommend solutions that deliver measurable business value.
- Explore, create, and help connect AI\-powered systems your teammates can use without needing to be AI experts themselves.
- Train, coach, and support marketing teams on prompt engineering, AI workflows, and responsible AI practices to drive adoption across the organization.
- Measure and report on the impact of AI initiatives, including productivity gains, campaign performance, content velocity, adoption, and marketing effectiveness.
- Maintain documentation and mapping of AI Marketing skills, systems, projects, and workflows in addition to changes in standard operating procedures for marketing’s AI use and development.
- Help establish governance, quality standards, and processes to ensure AI\-generated outputs are accurate, compliant, secure, and aligned with Delinea's brand and marketing standards.
What You Bring:
- 5\+ years of marketing experience across campaigns, content or product marketing.
- Demonstrated hands\-on AI experience. You’ve used AI tools to build something, whether that’s a skill, an automation or a workflow, and you can show your work.
- Some form of AI training, whether formal coursework, certifications or serious selfdirected learning.
- A systems mindset. You see the process behind the output and look for ways to make it repeatable and scalable.
- Comfort moving between strategy and execution, paving new ways between unknown and known, in the same day.
- Strong writing and communication skills. You can explain a technical workflow to a non\-technical stakeholder.
- Experience with marketing automation and AI and data \& intent signal tools, Make, HubSpot, Salesforce, Zapier, Anthropic (Claude), PowerBI, 6sense, DemandBase.
- Background in cybersecurity, SaaS or B2B technology marketing.
- A portfolio or recorded examples of AI skills, agents or automations you’ve built.
You’re a Great Fit If:
- You’re curious by default and treat every new AI tool as something to test, not fear.
- You’re a self\-starter who doesn’t wait for a playbook that doesn’t fully exist yet and you get things done.
- You collaborate well and know the best systems get built with input from the people using them.
- You hold yourself to a high bar for detail while keeping the bigger picture in view. You’re an independent thinker, who can evaluate and
- You act with integrity, respect security governance processes, especially when the tools you’re using are new enough that the guardrails are still being built.
- You’re comfortable in a fast\-moving environment where the org, tools and workflows keep evolving.
- You’re ready for a career and skill\-building adventure.
Why work at Delinea?
- We're passionate problem\-solvers helping the world's largest organizations protect what matters most: their human and machine identities.
- We invest in people who are smart, self\-motivated, and collaborative.
- What we offer in return is meaningful work, a culture of innovation and great career progression.
At Delinea, our core values are STRONG and guide our behaviors and success:
- Spirited \- We bring energy and passion to everything we do
- Trust \- We act with integrity and deliver on our commitments
- Respect \- We listen, value different perspectives, and work as one team
- Ownership \- We take initiative and follow through
- Nimble \- We adapt quickly in a fast\-changing environment
- Global \- We embrace diverse people and ideas to drive better outcomes
We believe weaving these core values into our day\-to\-day actions, and our process for hiring, evaluating, and promoting employees, helps us cultivate a work environment that embraces collaboration and camaraderie.
We take care of our employees. We offer competitive salaries, a meaningful bonus program, and excellent benefits, including healthcare insurance, as well as pension/retirement matching, comprehensive life insurance, an employee assistance program, time off plans, and paid company holidays.
*Delinea is an Equal Opportunity and Affirmative Action employer and prohibits discrimination and harassment of any type with regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.*
*Upon conditional offer of employment, candidates are required to complete comprehensive criminal background check, verification of education, and verification of employment, per employment policy. In addition, all publicly posted social media sites may be reviewed.*
Compensation Range: $120,560 \- $150,700
Salary Context
This $120K-$150K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Delinea, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($135K) sits 37% below the category median. Disclosed range: $120K to $150K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Delinea AI Hiring
Delinea has 2 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Based in Redwood City, CA, US. Compensation range: $150K - $162K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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