Interested in this AI/ML Engineer role at NRG Energy?
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About This Role
Welcome to the intersection of energy and home services. At NRG, we’re all about propelling the next generation of leaders forward. We are driven by our passion to create a smarter, cleaner and more connected future. We deliver innovative solutions that make our customers’ lives easier—helping them power, protect, and intelligently manage their homes and businesses. To do this, we need creative and talented people to join our company.
We offer a dynamic work environment and a unified and inclusive culture. NRG fosters a strong sense of belonging that leads to better collaboration and business performance. Our company programs are designed to help employees develop the skills they need for success now and in the future. In everything we do, we aim to champion our employees and bring value to our customers, investors and society.
More information is available at www.nrg.com. Connect with NRG on Facebook, Instagram, LinkedIn and X.
Job Summary:
NRG Energy is seeking a Data Science Analyst – Agentic AI Developer to design, develop, and deploy AI-powered solutions that improve decision-making, automate business processes, and generate actionable insights across the organization. This role combines data science, machine learning, software engineering, and agentic AI development to solve complex business challenges in the energy industry. The ideal candidate is comfortable working across the full AI lifecycle—from data acquisition and model development to deployment, monitoring, and continuous improvement—and thrives in a collaborative, fast-paced environment.
Essential Duties/Responsibilities:
- Collect, clean, integrate, and preprocess large structured and unstructured datasets to support advanced analytics, machine learning, and AI applications.
- Design, develop, deploy, and optimize machine learning models for forecasting, predictive analytics, anomaly detection, recommendation systems, classification, and operational optimization.
- Build AI-powered applications using large language models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, APIs, and workflow automation to improve productivity and streamline business processes.
- Rapidly prototype, test, and deploy scalable AI solutions, translating ambiguous business challenges into practical, production-ready applications.
- Develop scalable data pipelines, reusable software components, APIs, and AI workflows that integrate enterprise data sources and operational systems.
- Collaborate with business stakeholders, software engineers, data engineers, analysts, and subject matter experts to integrate AI capabilities into business workflows.
- Analyze complex datasets to identify trends, generate actionable insights, and communicate findings through dashboards, reports, and data visualizations for both technical and non-technical audiences.
- Monitor, evaluate, and continuously improve AI models and applications to ensure accuracy, scalability, reliability, governance, and responsible AI practices.
- Stay current with emerging AI, machine learning, and data science technologies, evaluating opportunities to drive innovation and business value.
Working Conditions:
- Fully in-office position based in Houston, TX or Princeton, NJ, with flexibility to work outside normal business hours as needed to support critical business operations and project milestones.
Minimum Requirements:
- Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Quantitative Finance, or a related quantitative discipline, completed or expected to be completed by December 2026; Master’s degree preferred.
- Zero or more years of professional experience in data science, machine learning, AI application development, or advanced analytics.
Preferred Qualifications:
- Experience with LangGraph, LangChain, Semantic Kernel, CrewAI, or similar orchestration frameworks; NLP, Generative AI, Computer Vision, Spark, Databricks, MLflow, Kubernetes, time-series forecasting, energy markets, or cloud-native AI services (e.g., Azure OpenAI, Amazon Bedrock, Google Vertex AI) is highly desirable
- Strong proficiency in Python and SQL, with experience processing large datasets and developing production-quality software.
- Experience with machine learning frameworks such as Scikit-learn, TensorFlow, PyTorch, XGBoost, or similar technologies.
- Experience developing AI applications using LLMs, prompt engineering, RAG, AI agent frameworks, vector databases, REST APIs, and modern software development tools (Git, Docker).
- Experience with cloud platforms (AWS, Azure, or Google Cloud), data visualization tools (Power BI, Tableau, Plotly, or Matplotlib), and building scalable data pipelines.
- Strong understanding of statistics, feature engineering, model evaluation, and MLOps fundamentals.
- Excellent analytical, communication, and problem-solving skills, with the ability to independently manage multiple priorities, collaborate across multidisciplinary teams, and deliver high-quality AI solutions with professionalism, integrity, and a commitment to continuous learning.
NRG Energy is committed to a drug and alcohol-free workplace. To the extent permitted by law and any applicable collective bargaining agreement, employees are subject to periodic random drug testing, and post-accident and reasonable suspicion drug and alcohol testing. EOE AA M/F/Protected Veteran Status/Disability. Level, Title and/or Salary may be adjusted based on the applicant's experience or skills.
EEO is the Law Poster (The poster can be found at http://www.eeoc.gov/employers/upload/poster_screen_reader_optimized.pdf)
Official description on file with Talent.
We support the use of AI tools to help you prepare for your interview (e.g., practicing responses, researching the role, or refining your resume). However, during interviews and assessments, we expect responses to reflect your own thinking, experience, and communication. Use of AI to generate or read answers in real time, complete assessments, or misrepresent your qualifications is not permitted and may impact your candidacy.
The base salary range for this position is: $65,000 - $75,000* *The base salary range above represents the low and high end of the salary range for this position. Actual salaries will vary based on several factors including but not limited to location, experience, and performance. The range listed is just one component of the total compensation package for employees. Other rewards may include annual bonus, short- and long-term incentives, and program-specific awards. In addition the position may be eligible to participate in the benefits program which include, but are not limited to, medical, vision, dental, 401K, and flexible spending accounts.
Salary Context
This $65K-$75K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At NRG Energy, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($70K) sits 67% below the category median. Disclosed range: $65K to $75K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
NRG Energy AI Hiring
NRG Energy has 3 open AI roles right now. They're hiring across AI/ML Engineer, MLOps Engineer. Positions span Princeton, NJ, US, Washington, United States, Lehi, UT. Compensation range: $75K - $75K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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