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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.
About the role
Delinea is looking for a strategic, hands-on Director, People Business Partner to partner with our Product, Technology, and AI leaders. You'll be the main people advisor to the EVP of Product & Technology and the AI leader. You'll help shape how we organize, hire, develop, and keep the technical talent behind our identity security platform. This role sits on the Global PBP leadership team and helps set the people strategy for the whole company.
This is a pivotal moment to join. Product & Technology is moving to a General Manager (GM) model: the EVP of Product & Technology will lead three GMs, each owning a specialized vertical. You'll be a key partner in making this model work. You'll help leaders set decision rights, define how the GMs and shared functions work together, and adapt talent, org design, and performance practices to fit.
What you'll do
- GM operating model design: Partner with the EVP of Product & Technology and the three General Managers to design and embed the GM operating model:
- Define decision rights across the GMs, the EVP, and shared functions (who decides, who is consulted, who executes) so accountability is clear and decisions move faster.
- Design how each GM business works with shared functions such as Engineering platform, Product Ops, Finance, and the People team, including interfaces, governance forums, and escalation paths.
- Adapt org design, talent, and performance practices to the GM model: span-and-layer design, leadership roles and capability gaps, succession, goal setting, and how performance and rewards reflect GM-level accountability.
- Lead the people side of the change, including sequencing, leader readiness, communications, and adoption metrics.
- Enterprise people strategy: Use what the GM transformation teaches us to shape people programs and practices across Delinea:
- Turn what works in P&T into repeatable principles, playbooks, and tools for decision rights, org design, and performance practices.
- Partner with the Global PBP team and the People COEs (Talent, Total Rewards, People Ops) to adapt and scale these approaches to other functions where they fit.
- Act as a thought partner to the VP, Global PBPs on enterprise org effectiveness, and help build operating model and org design capability across the PBP team.
- Strategic partnership: Act as a trusted advisor to P&T and AI leaders and their staff. Turn business priorities into people plans covering org design, talent, and culture.
- Org design & workforce planning: Lead org design, span-and-layer reviews, and headcount planning. Plan how AI adoption changes roles, skills, and team structures.
- Talent & succession: Run talent reviews, succession planning, and calibration. Find critical engineering and product talent and build plans to keep them.
- Leadership coaching: Coach senior leaders and managers on team effectiveness, performance, and change. Build leadership capability across the org.
- Change management: Guide teams through reorganizations, integrations, and operating model changes.
- Employee relations: Handle complex ER matters with sound judgment, working with Legal and the People COEs.
- Total rewards & performance: Work with Total Rewards on technical career frameworks, leveling, offers, and annual pay and performance cycles.
- Data-driven insights: Use people data (attrition, engagement, hiring speed, org health) to spot trends and drive decisions.
- Culture & engagement: Act on engagement survey results and build an inclusive, high-performing engineering culture.
What you bring
- 10+ years of progressive HR experience, including 5+ years as a senior HRBP supporting Engineering, Product, or R&D in a B2B SaaS or tech company
- Experience partnering with C-suite or EVP-level leaders
- A track record in org design, workforce planning, and change management
- Experience partnering with an organization that runs a GM or business-unit model, ideally during a move into one. You understand GM accountability, decision rights, and how GMs work with shared functions, and you can coach leaders who are new to the model as well as those who have run it before.
- Working knowledge of how software teams operate (agile, product/engineering career paths, technical leveling)
- Curiosity about AI and how it's reshaping work, roles, and skills
- Strong judgment in employee relations, plus sound knowledge of employment law in multiple countries
- Comfort with data and with HR systems (e.g., Workday)
- Executive presence and the ability to challenge leaders constructively
- Global experience preferred (US, EMEA, APAC, LATAM)
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: $170K - $200K
Salary Context
This $170K-$200K 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 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 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 ($185K) sits 13% below the category median. Disclosed range: $170K to $200K.
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.
Delinea AI Hiring
Delinea has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Redwood City, CA, US. Compensation range: $200K - $200K.
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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