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About This Role
At GE Vernova, we’re focused on accelerating the energy transition by helping the world electrify and decarbonize. As the business evolves, we are also reimagining how HR works \- embedding AI into how we serve our people leaders, employees, the business, and ourselves. Our HR function is moving from early experimentation to disciplined adoption, and we’re investing in the people, practices, and use cases that will make that shift real.
We are seeking an HR AI Enablement \& Innovation Program Manager to drive how the HR function builds AI fluency, scales practical AI solutions, and learns from itself. This role sits within HR Enablement \& Simplification and connects HR teams to the AI capabilities that amplify their work.
This is an opportunity for a builder \- someone who can shape what AI capability looks like for HR, run a function\-wide community, and stand up working AI prototypes inside HR, all while partnering closely with the roles shaping AI governance, workforce transformation, and Workday AI enablement across the enterprise.
Job Description
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Role Summary
The HR AI Enablement \& Innovation Program Manager leads three connected workstreams for the HR function: AI fluency and skill\-building, the HR AI Community of Practice, and HR AI innovation and use case incubation. Fluency is the foundation \- the capability the other two pillars stand on. Without it, the community has nothing to share and innovation has no one to adopt it.
This role partners closely with the AI Governance Project Manager (responsible AI guardrails), the AI Workforce Transformation \& Organizational Design Project Manager (enterprise\-wide adoption and Lean), and the Workday AI Enablement Lead (Workday\-native AI capability). Together, these four roles form the operating model for how GE Vernova HR adopts AI.
Key Responsibilities:
*HR AI Fluency \& Skill\-Building*
- Own and evolve the HR function’s AI learning roadmap \- from foundational fluency to advanced application across HR Client Support, COEs, and HR leadership.
- Build on existing GE Vernova HR enablement assets to define what AI\-specific capability looks like for HR: prompt engineering, agent orientation, responsible use, data interpretation, and human\-in\-the\-loop judgment.
- Partner with HR4HR and Learning teams to embed AI skill\-building into existing development pathways rather than creating parallel structures.
- Curate high\-quality external content and translate it into GE Vernova\-relevant practice, recognizing that AI fluency is increasingly built through hands\-on, continuous learning rather than long formal courses.
- Increase responsible HR usage of Copilot, AMP, and other enterprise standard AI tools.
*HR AI Community of Practice*
- Build and operate the HR AI Community of Practice as a sustained capability \- owning the cadence, content, contributors, curator network, and operating rhythm.
- Lead monthly working sessions and quarterly prototype showcases that move the function from individual experimentation to collective progress.
- Develop and steward a peer champion network across HR COEs and HR Client Support teams to create consistency in practice, accelerate adoption, and reinforce governance evenly across the function.
- Build and maintain a shared library of HR AI use cases, prompts, prototypes, playbooks, and lessons learned that any HR team can draw from.
- Measure community health and impact: participation, contribution, use case adoption, and skill progression.
*HR AI Innovation \& Use Case Incubation*
- Partner with HR COEs and HR Client Support to identify, scope, and prototype AI solutions that solve real HR problems \- service delivery, knowledge access, content generation, analytics, and decision support.
- Run a lightweight intake and triage model that routes HR use cases to the right path: Workday\-native (with the Workday AI Enablement Lead), Copilot/AMP/standard tools (this role), bespoke (with DT), or “not yet” (with Governance).
- Build and maintain a working HR AI prototype portfolio \- running pilots, capturing outcomes, and graduating successful prototypes into sustained capabilities.
- Apply citizen\-developer principles within HR to expand who can build, recognizing that generative AI has shifted the fluency bar from technical coding to clear problem reasoning.
- Distinguish HR\-internal innovation (this role’s scope) from enterprise\-wide AI transformation (the AI Workforce Transformation \& Organizational Design Project Manager’s scope)—partnering closely where the two intersect.
*Cross\-Role Partnership \& Operating Discipline*
- Maintain clear lanes with the AI Governance Project Manager, AI Workforce Transformation \& Organizational Design Project Manager, and Workday AI Enablement Lead—translating governance principles into HR practice, surfacing organization and workforce implications, and routing technology decisions to the right owner.
- Develop and maintain an HR AI Maturity view to track fluency, adoption, innovation pipeline, and value across HR \- informing investment decisions and surfacing where capability\-building is working.
- Track adoption, skill\-building, and value\-realization metrics for HR AI work.
- Stay current on external HR AI practices and translate insights into practical recommendations.
- Review materials with labor and employment counsel before broad release of guidance, prompts, or AI\-enabled HR content.
Required Qualifications
- Bachelor’s degree required; advanced degree in Human Resources, Organizational Development, Learning, Business, Technology, or related field preferred.
- 8\+ years of experience in HR enablement, learning and development, HR transformation, change management, or enterprise program management.
- Demonstrated ability to define and scale capability\-building programs at scale in partnership with Learning teams.
- Hands\-on familiarity with generative AI tools and a practical understanding of how AI is being applied in HR.
Preferred Experience
- Strong facilitation, community\-building, and stakeholder management skills.
- Proven ability to lead cross\-functional initiatives and influence without direct authority.
- Analytical, structured, and outcome\-oriented approach to execution.
- Strong written and verbal communication, including the ability to translate technical concepts into clear, actionable guidance for HR audiences
- Experience in a large, matrixed, global organization.
- Background in HR analytics, HR technology, HR operations, or HR learning and development.
- Exposure to generative AI tools (Copilot, Claude, ChatGPT, Workday AI) in an enterprise setting.
- Experience running communities of practice, champion networks, or citizen\-developer programs.
- Familiarity with adult learning, instructional design, or capability\-building frameworks.
What Success Looks Like
The ideal candidate is:
- A builder who turns ambiguous opportunity into structured, repeatable practice.
- A connector who can move fluently between HR practitioners, technology partners, and senior leaders.
- Comfortable in the messy middle of AI adoption \- where guidance is still forming and prototypes outnumber polished solutions.
- Energized by helping others build skills, confidence, and judgment in a fast\-moving domain.
- Disciplined about measurement, value realization, and operating rhythm.
Why Join GE Vernova?
This role helps shape how a global energy company brings AI into the HR function—building the skills, community, and solutions that make adoption real. You’ll work alongside a small set of peers redefining how HR operates, with visibility across HRLT and the businesses we serve. If you want to build something durable in a domain that’s still being defined, this is the seat.
Additional Information
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GE Vernova offers a great work environment, professional development, challenging careers, and competitive compensation. GE Vernova is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other characteristics protected by law.
GE Vernova will only employ those who are legally authorized to work in the United States for this opening. Any offer of employment is conditioned upon the successful completion of a drug screen (as applicable).
Relocation Assistance Provided: No
\#LI\-Remote \- This is a remote position
Application Deadline: June 14, 2026
For candidates applying to a U.S. based position, the pay range for this position is between $105,800\.00 and $176,400\.00\. The Company pays a geographic differential of 110%, 120% or 130% of salary in certain areas. The specific pay offered may be influenced by a variety of factors, including the candidate’s experience, education, and skill set.
Bonus eligibility: discretionary annual bonus.
This posting is expected to remain open for at least seven days after it was posted on May 29, 2026\.
Available benefits include medical, dental, vision, and prescription drug coverage; access to Health Coach from GE Vernova, a 24/7 nurse\-based resource; and access to the Employee Assistance Program, providing 24/7 confidential assessment, counseling and referral services. Retirement benefits include the GE Vernova Retirement Savings Plan, a tax\-advantaged 401(k) savings opportunity with company matching contributions and company retirement contributions, as well as access to Fidelity resources and financial planning consultants. Other benefits include tuition assistance, adoption assistance, paid parental leave, disability benefits, life insurance, 12 paid holidays, and permissive time off.
GE Vernova Inc. or its affiliates (collectively or individually, “GE Vernova”) sponsor certain employee benefit plans or programs GE Vernova reserves the right to terminate, amend, suspend, replace, or modify its benefit plans and programs at any time and for any reason, in its sole discretion. No individual has a vested right to any benefit under a GE Vernova welfare benefit plan or program. This document does not create a contract of employment with any individual.
Salary Context
This $105K-$176K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At GE Vernova, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($141K) sits 22% below the category median. Disclosed range: $105K to $176K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
GE Vernova AI Hiring
GE Vernova has 6 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Greenville, SC, US, Niskayuna, NY, US, Remote, US. Compensation range: $148K - $254K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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