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We're looking for a
Principal Strategic Consultant (AI Delivery)
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This role is Remote, United States
Cornerstone is seeking a Principal Strategic Consultant (AI Delivery) to lead the successful implementation and deployment of AI solutions across global enterprise customers.
This role is responsible for driving delivery excellence across Workforce AI (Intelligence\+, Skills Architect, PeopleGraph, Agent Packs, and emerging AI\-powered solutions). You will coordinate cross\-functional teams, manage executive customer relationships, oversee customer execution from Solution Design handoff through HyperCare, and ensure customers achieve measurable business outcomes from their Workforce AI investments.
The ideal candidate combines enterprise SaaS delivery leadership, strategic consulting, program management, customer engagement, and operational execution expertise.
In this role you will…
### Delivery Leadership
- Own end\-to\-end customer execution from Solution Design handoff through HyperCare.
- Develop delivery plans, implementation strategies, and governance frameworks.
- Establish project milestones, success measures, and operational readiness criteria.
- Ensure successful deployment, customer launch readiness, adoption, and transition to Customer Success.
### Customer \& Stakeholder Management
- Serve as the primary delivery leader for customer engagements.
- Act as the trusted delivery advisor for executive customer stakeholders, ensuring implementation decisions remain aligned with agreed business outcomes.
- Facilitate executive steering committees and status reviews.
- Build strong relationships with customer stakeholders.
- Manage expectations, risks, dependencies, assumptions, decisions, and executive escalations.
### Cross\-Functional Coordination
- Coordinate Advisory, Solution Design, Product Management, Engineering, Forward Deployment Engineering (FDE), Customer Success, Sales, Delivery Services, and customer stakeholders.
- Ensure alignment between solution definition and implementation execution.
- Drive testing, validation, launch readiness, and deployment activities.
- Support transition through HyperCare to Customer Success, ensuring operational readiness and long\-term adoption.
### Delivery Governance
- Lead implementation governance and executive milestone reviews.
- Ensure delivery gates and readiness criteria are achieved before progressing through implementation phases.
- Track delivery health and proactively resolve delivery risks.
### Operational Excellence
- Establish repeatable delivery processes, governance models, and implementation best practices.
- Identify opportunities to improve implementation efficiency and scalability.
- Contribute to the Workforce AI Delivery Methodology, Playbook, and reusable implementation assets.
- Capture lessons learned to continuously improve global delivery.
You have what it takes if you have…
- 8\+ years of experience in enterprise SaaS delivery, implementation leadership, program management, consulting, or customer execution.
- Experience managing complex enterprise customer implementations.
- Strong executive stakeholder management skills.
- Experience leading cross\-functional teams in matrixed organizations.
- Exceptional communication and organizational skills.
- Can travel up to 25% for client site visits
Extra dose of awesome if you have…
- Workforce technology experience
- HR technology experience
- Enterprise software implementation experience
- Global customer delivery experience
- Experience working with AI\-enabled software solutions
\#LI\-remote
Our Culture:
Spark Greatness. Shatter Boundaries. Share Success. Are you ready? Because here, right now – is where the future of work is happening. Where curious disruptors and change innovators like you are helping communities and customers enable everyone – anywhere – to learn, grow and advance. To be better tomorrow than they are today.
Who We Are:
At Cornerstone, we believe in AI that works in the service of people, amplifying their judgment to drive high\-performing, future\-ready organizations forward. Cornerstone Workforce AI™, the intelligence platform for workforce readiness, brings together workforce and labor market data into a proprietary Cornerstone People Graph™, translating signals into intelligence, targeting learning where it matters, developing critical skills, and surfacing hidden talent. Delivered as an open, enterprise platform across whatever application your people work in every day, Cornerstone Workforce AI is built for scale, security, and trust, with certified AI guardrails. As an industry leader, Cornerstone is helping approximately 7,000 organizations, 140M\+ users, across 186 countries build continuous workforce readiness.
Total Rewards:
At Cornerstone, we are dedicated to inspiring excellence and pushing boundaries in everything we do. Our compensation strategy is based on three fundamental principles: equitable pay, market\-driven research, and skill\-based appraisals. As part of our mission to share success and empower individuals to thrive in an ever\-changing world, the listed salary range is just one element of Cornerstone’s comprehensive compensation package. This compensation package may also include annual bonuses, short\- and program\-specific awards depending on the role, and a comprehensive benefit offering. The disclosed salary range reflects the geographic differential based on the location of the position if applicable. The starting salary for the successful applicant will depend on several job\-related factors, including education, training, experience, certifications, location, business needs, and market demands. This range is based on a full\-time position and may be adjusted in the future. Join us in shaping the future of work — tomorrow, together. Experience flexibility and empowerment in your career at Cornerstone. The BASE salary range for this position is: 128500 \- 205600 USD.
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Equal Employment Opportunity has been, and will continue to be, a fundamental commitment at Cornerstone OnDemand. All qualified applicants are given consideration regardless of race, religion, color, gender, sex, age, sexual orientation, gender identity, national origin, marital status, citizenship status, disability, veteran status, or any other protected class as provided in applicable Federal, State, or Local fair employment laws. If you have a disability or special need that requires accommodation, please contact us at [email protected] or \+1 855 454 8433\.
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Salary Context
This $128K-$205K range is below the median 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 Cornerstone OnDemand, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($167K) sits 22% below the category median. Disclosed range: $128K to $205K.
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.
Cornerstone OnDemand AI Hiring
Cornerstone OnDemand has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $205K - $205K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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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