Interested in this AI/ML Engineer role at Westinghouse Electric Company, LLC?
Apply Now →Skills & Technologies
About This Role
Are you interested in being part of an innovative team that supports Westinghouse’s mission to provide clean energy solutions? At Westinghouse, we recognize that our employees are our most valuable asset and we seek to identify, attract and recruit the most qualified talent while recognizing and encouraging the value of diversity in the global workplace.
About the role:
As a Manager \- Data Science, you will lead an international team. This team consists of data scientists and machine learning practitioners responsible for developing, experimenting with, deploying, and benchmarking AI/ML models. Additionally, they will work on prompt\-engineered solutions embedded into Westinghouse digital products. You will work with Westinghouse leaders to understand business and product need to support main themes such as Asset Management, Work Management, and Business Process Optimization. Identify critical skill needs and address gaps to ensure the necessary capabilities—spanning classical ML, deep learning, and generative AI—are available to help develop scalable, production\-grade model solutions.
You will report to the Director, AI Data Infrastructure and work remotely.
Key Responsibilities:
- Engage with the Digital and Innovation (D\&I) leaders to promote the D\&I vision. This vision drives global Westinghouse and industry modernization. To achieve this, provide input and accomplish the digital roadmap. The roadmap aims to improve the position of Westinghouse and our customers relative to safety, profitable growth, and process efficiency.
- Assume responsibility for the success of model development, experimentation, deployment, quality assurance, and prompt engineering practices allowing Westinghouse digital solutions.
We will achieve this responsibility through:
- Lead the data science team in developing, evaluating, and deploying AI/ML models and generative AI solutions. These solutions address customer needs across multiple use cases. Ensure models are production\-ready, benchmarked against clear performance criteria, and embedded into digital solutions.
- Train team members responsible for model deployment and monitoring in production. Establish best practices for experimentation, model validation, reproducibility, versioning, and responsible AI according to Westinghouse Information Technology and the AI Architecture team.
Qualifications:
- A data science, machine learning, or applied AI management background leading international team members
- Familiarity with the nuclear and energy industries
- Experience with model development, experimentation frameworks, MLOps practices, and 3rd party AI/ML platforms and foundation models
- B.S. degree in business management, computer science, data science, industrial engineering, statistics or equivalent
- 10\+ years of experience in business, operations or consultation services with progression and complexity
- Microsoft Certified: Azure Data Engineer Associate helpful Microsoft Certified: Azure Database Administrator Associate helpful
We are committed to transparency and equity in all of our people practices. The base salary range for this position, which is dependent upon experience, qualifications and skills, is estimated to be $130,400 to $163,000 per year.
\#LI\-Remote
Why Westinghouse?
Our benefits package is tailored to meet the diverse needs of our employees, while also promoting wellness and career growth. The following are representative of what we offer:
- Comprehensive Medical benefits which could include medical, dental, vision, prescription coverage and Health Savings Account (HSA) with employer contributions options
- Wellness Programs designed to support employees in maintaining their health and well\-being including Employee Assistance Program providing support for our employees and their household members
- 401(k) with Company Match Contributions to support employees' retirement
- Paid Vacations and Company Holidays
- Opportunities for Flexible Work Arrangements to promote work\-life balance
- Educational Reimbursement and Comprehensive Career Programs to help employees grow in their careers
- Global Recognition and Service Programs to celebrate employee accomplishments and service
- Employee Referral Program
Westinghouse Electric Company is the global nuclear energy industry's first choice for safe, clean, and efficient energy solutions. We enable our delivery of this vision by living our value system:
- Safety and Quality
- Integrity and Trust
- Customer Focus and Innovation
- Speed and Passion to Win
- Teamwork and Accountability
While our Global Headquarters are located in Cranberry Township, PA, we have over 11,000 employees working at locations in 19 different countries. You can learn more by visiting http://www.westinghousenuclear.com.
Westinghouse is an Equal Opportunity Employer including Veterans and Individuals with Disabilities
Salary Context
This $130K-$163K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Westinghouse Electric Company, LLC, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($146K) sits 33% below the category median. Disclosed range: $130K to $163K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Westinghouse Electric Company, LLC AI Hiring
Westinghouse Electric Company, LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Cranberry Township, PA, US. Compensation range: $163K - $163K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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.