Interested in this AI/ML Engineer role at Delinea?
Apply Now →Skills & Technologies
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.
Job Summary
AI agents are moving into the enterprise fast, and securing them is one of Delinea's most important bets. As Director of Engineering for AI security, you will build and lead the engineering organization that discovers AI agents, gives them identity, and controls what they can do at the moment they act. You will own engineering delivery across the entire AI security platform and act as the engineering counterpart to the product leader for this area. This is a build\-and\-scale role: you will grow the team that delivers the platform and scale the technology from early customers to broad production use. This role will report directly into our VP of AI Product Engineering.
Leadership Responsibilities
- Build and lead a multi\-team engineering organization for AI security, hiring and developing engineering managers, tech leads, and senior engineers.
- Serve as the single engineering owner for AI security, accountable opposite the product leader for what the organization ships and when.
- Grow the team across the US and Mexico while protecting quality, security rigor, and a strong engineering culture.
- Set engineering standards and ways of working, and represent the organization to senior leadership and customers.
What You'll Do
- Define and drive the engineering roadmap for Delinea's AI security platform, from discovering AI agents to controlling what they can do at runtime.
- Lead the teams building agent discovery and posture, agent identity and authorization, the AI gateway, and integrations with platforms such as Microsoft Copilot and Salesforce Agentforce.
- Own the key technical decisions, including the identity and authorization foundations the rest of the platform builds on.
- Ensure the organization builds on Delinea's shared platform services rather than duplicating discovery, identity, or enforcement.
- Partner with product, architecture, and field teams, and work directly with design and early customers to validate what you build.
- Scale the technology from early customer deployments to broad production use, at the reliability, correctness, and auditability a security product demands.
What You'll Need
- Typically 12 or more years in software engineering, with several years leading engineering teams, including leading other leaders or senior engineers.
- A track record of building and scaling engineering organizations and delivering complex backend or platform products to production.
- Strong technical depth in one or more of identity, authorization, cloud security, or distributed systems, enough to own architecture\-level decisions.
- Experience owning a product or platform area end to end in close partnership with product management.
- Experience hiring, developing, and retaining engineers and managers.
We'd Love to See
- Experience securing AI, agents, or large language model systems.
- Background in privileged access, identity security, or zero trust.
- Experience taking a new product from early customers to general availability.
- Experience handling and scaling data pipelines.
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: $180K \- $220K
Salary Context
This $180K-$220K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($200K) sits 9% below the category median. Disclosed range: $180K to $220K.
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.
Delinea AI Hiring
Delinea has 6 open AI roles right now. They're hiring across AI Product Manager, AI Software Engineer, AI/ML Engineer. Based in Redwood City, CA, US. Compensation range: $162K - $220K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.