Interested in this AI/ML Engineer role at GE Vernova?
Apply Now →About This Role
At GE Vernova, we’re focused on accelerating the energy transition by helping the world electrify and decarbonize. As the company continues to evolve, we are reimagining how work gets done—leveraging AI, automation, robotics, and modern operating models to drive greater speed, scale, efficiency, and employee impact. We are seeking an AI Workforce Transformation \& Organizational Design Project Manager to help shape the future of work across GE Vernova. This role sits at the intersection of strategy, people, process, and technology and will play a key part in translating emerging capabilities into practical workforce, organization design, and operating model solutions.
This is an exciting opportunity for a forward\-thinking leader who thrives on complexity, can influence across a matrixed enterprise, and is energized by the challenge of building new ways of working that enable both business performance and a great employee experience.
Job Description
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Role Summary
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The AI Workforce Transformation \& Organizational Design Project Manager will lead enterprise\-wide efforts to integrate AI, robotics, and automation into how work is designed, organized, and executed. This individual will partner closely with business leaders, Supply Chain, HR, Finance, DT, and other teams to identify AI opportunities, shape future\-state models, and ensure transformation efforts deliver measurable business value.
The ideal candidate combines strong program leadership with organizational design expertise, a practical understanding of AI\-enabled change, and the ability to connect strategic ambition with real\-world execution.
Key Responsibilities
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### Enterprise AI Transformation \& Program Leadership
- Lead and contribute to cross\-functional initiatives to identify, pilot, and implement AI\-enabled solutions across GE Vernova businesses and functions.
- Partner with business leaders, Supply Chain, HR, Finance, DT, and other teams to ensure AI initiatives incorporate organization implications required to effectively transition from current to future state.
- Assess the implications of AI adoption on organizational structure, role design, skills, workforce models, and employee experience.
- Build implementation roadmaps, governance structures, milestones, and success measures for AI transformation efforts, including tracking of financials/value realized.
- Ensure people, process, and business strategies are aligned to support scalable and sustainable adoption of AI solutions.
- Serve as a trusted advisor to Lean and continuous improvement efforts, ensuring AI opportunities are incorporated into process redesign.
- Identify opportunities to eliminate non\-value\-added work, improve efficiency, and enhance productivity through AI, automation, and robotics.
- Coordinate enterprise HR engagement in Robotics \& Automation initiatives across businesses and functions.
- Help establish consistent standards and best practices for the people side of automation transformation.
- Stay current on emerging AI, automation, and workforce trends and translate insights into practical recommendations for the business.
Required Qualifications
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- Bachelor’s degree required; advanced degree in Business, Human Resources, Organizational Psychology, Industrial Engineering, Technology, or a related field preferred.
- 8\+ years of experience in organizational effectiveness, HR transformation, process improvement, digital transformation, Lean, or enterprise program management.
- Proven experience leading complex, cross\-functional enterprise initiatives.
Preferred Experience
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- Strong understanding of organizational design, workforce planning, and job/role redesign.
- Experience with Lean methodologies, continuous improvement, or operational excellence programs.
- Familiarity with AI, automation, robotics, or digital transformation and their workforce implications.
- Strong stakeholder management, facilitation, and executive communication skills.
- Demonstrated analytical, project management, and change leadership capabilities.
- Experience in a large, matrixed, global organization.
- Background partnering with operations, manufacturing, or industrial businesses.
- Exposure to RPA, Generative AI, intelligent automation, or digital workforce initiatives.
What Success Looks Like
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The ideal candidate is:
- A systems thinker who connects technology, work, people, and process design.
- A strong program leader who can drive complex enterprise initiatives from strategy to execution.
- A trusted advisor who can influence senior leaders on the future of work.
- Comfortable moving between high\-level AI\-enabled operating model discussions and practical execution details.
Why Join GE Vernova?
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This role offers a unique opportunity to help shape how a global energy company adapts to the future of work. You’ll be part of a mission\-driven organization focused on innovation, transformation, and impact—working on challenges that matter at scale.
If you’re energized by building what’s next, influencing enterprise change, and helping teams work smarter in a rapidly evolving environment, this role could be an exciting fit.
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 04, 2026
For candidates applying to a U.S. based position, the pay range for this position is between $122,400\.00 and $203,900\.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 28, 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 $122K-$203K 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 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 $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 ($163K) sits 10% below the category median. Disclosed range: $122K to $203K.
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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