Interested in this AI/ML Engineer role at GE Vernova?
Apply Now →Skills & Technologies
About This Role
The Sustainability Metrics Governance AI Specialist is responsible for supporting the quality, governance, assurance, and continuous improvement of sustainability metrics and related data processes across GE Vernova. This individual will play a key role in executing data quality controls, managing sustainability data workflows, operationalizing reporting requirements, and identifying opportunities to leverage AI, automation, and advanced analytics to improve reporting efficiency, accuracy, and decision\-making across the enterprise.
Reporting to the Senior Sustainability Data Strategy and Analytics Leader, this position works closely with the Sustainability Director, Sustainability Controller, and Digital Technology (DT) teams. The role serves as an operational bridge between sustainability, data, and technology teams, translating strategic business requirements into scalable data solutions, governance processes, automation capabilities, and AI\-enabled workflows. This individual will support the execution of GE Vernova’s sustainability data strategy, analytics priorities, and AI roadmap to enhance data quality, governance, and reporting capabilities. This individual will be responsible to ensure all reported data adheres to internal guidelines and standards. This role will project manage and resolve any identified gaps or issues regarding reported data.
The successful candidate will support Corporate and segment sustainability teams in understanding their data, identifying reporting gaps, driving continuous improvement, and implementing best\-in\-class processes for data gathering, recordkeeping, and reporting. In addition, this role will help identify, develop, and deploy AI\- and automation\-enabled solutions that improve data quality, strengthen governance, reduce manual effort, and provide actionable insights. Working closely with business stakeholders, AI specialists, and DT teams, the individual will help bring strategic sustainability and digital transformation initiatives to life through practical, scalable solutions that support the organization's sustainability objectives.
Job Description
===================
Roles and Responsibilities
Data Strategy \& Governance
- Support the development and continuous improvement of GE Vernova’s sustainability data strategy.
- Translate reporting objectives into actionable data quality plans.
- Partner with DT/AI teams to convert business requirements into scalable solutions and governance processes.
- Ensure alignment with enterprise data standards and update guidelines as directed.
Audit \& Sustainability Metrics Monitoring
- Support Management Review Checklist (MRC) execution and external assurance/audit activities.
- Execute periodic monitoring of critical sustainability KPIs.
- Collaborate with leaders to resolve data quality issues.
- Expand monitoring scope beyond GHG emissions to energy, water, waste, and circularity.
AI Coordination \& Automation
- Support the development and implementation of AI\-enabled reporting and analytics.
- Manage project plans, timelines, and serve as a liaison between sustainability, DT, and AI stakeholders.
- Assist in designing, testing, and deploying automation tools; develop prototypes using Python, N8N, and Power BI.
Data Management \& Reporting
- Partner with operations to ensure sustainability data accuracy and completeness.
- Support quarterly reviews, board reporting, and regulatory disclosures.
- Validate ESG metrics (GHG, energy, water, waste) and identify process improvement opportunities.
- Support assurance activities for voluntary and mandatory reporting.
Project Management \& Continuous Improvement
- Conduct data gap assessments and coordinate remediation.
- Drive continuous improvement initiatives to enhance data quality and reporting effectiveness.
- Manage project plans, track milestones, and ensure timely, accurate reporting.
Policy \& Standard Work Development
- Develop/maintain SOPs for data collection, management, and reporting.
- Support alignment with frameworks like CSRD, ESRS, and GHG Protocol.
- Promote consistent reporting practices across the organization.
Required Qualifications
- Bachelor’s degree in a relevant field and a minimum of 5 years of relevant professional experience; or master’s degree in a relevant field and a minimum of 3 years of relevant professional experience.
Desired Characteristics
- Expertise: Knowledge of GHG Protocol, CSRD, ESRS, SEC, and EU Taxonomy.
- Assessment: Experience evaluating the quality, accuracy, and completeness of ESG disclosures.
- Skills: Strong analytical, project management, and communication skills.
- Technical: Proficiency in MS Office, Power BI, Tableau, SQL, Python, R, and AI/automation concepts.
- Mindset: Independent, collaborative, results\-oriented, and passionate about process improvement
Additional Information
==========================
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: August 12, 2026
For candidates applying to a U.S. based position, the pay range for this position is between $94,700\.00 and $157,700\.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 August 05, 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 $94K-$157K range is in the lower quartile 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 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($126K) sits 41% below the category median. Disclosed range: $94K to $157K.
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.
GE Vernova AI Hiring
GE Vernova has 9 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist, Research Engineer. Positions span Niskayuna, NY, US, Remote, US. Compensation range: $148K - $219K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.