Interested in this AI/ML Engineer role at National Grid?
Apply Now →About This Role
Team Data, Analytics and Insights Business Unit IT and Digital Requisition No. 11872 City Brooklyn, US, 11217 Syracuse, US, 13202 Waltham, US, 02451 Date posted 30 September 2026
Lead AI Strategy & Value Specialist
Every day we deliver safe and secure energy to homes, communities, and businesses. We are there when people need us the most. We connect people to the energy they need for the lives they live. The pace of change in society and our industry is accelerating and our expertise and track record puts us in an unparalleled position to shape the sustainable future of our industry.
To be successful we must anticipate the needs of our customers, reducing the cost of energy delivery today and pioneering the flexible energy systems of tomorrow. This requires us to deliver on our promises and always look for new opportunities to grow, both ourselves and our business.
IT and Digital works in a harmonised partnership with the National Grid group of diverse energy businesses to deliver technology which revolutionises the way we operate. As we lead the charge towards a carbon-free future, our teams are embracing disruptive changes in our industry by working with Agile methodologies and adopting Digital mindsets to drive efficiency and bring new capabilities for our internal and external customers.
Our work here is critical. National Grid moves energy to millions of homes and businesses in the UK and US and the technology we utilise to complete that task is down to us. The successful applicant for this position will be an integral contributor towards this goal and we will support your professional development as part of our multi-cultural, customer-centric global team.
This role operates under a hybrid working model and is based out of the Brooklyn, NY, Syracuse, NY or Waltham, MA. Hybrid work expectations are subject to business needs and company policy.
Job Purpose
The AI Value Office Consultant is a US-based individual-contributor role responsible for helping business teams identify, shape, assess and progress high-value artificial intelligence opportunities. Reporting to the AI Value Office Director, the role operates as a hands-on internal consultant and a key Value Office interface for relevant US business stakeholders, while working as part of one integrated UK/US team.
The Consultant supports opportunities through the end-to-end AI value lifecycle: origination and intake, problem framing, value and feasibility assessment, business-case development, prioritization, delivery partnership and value realization. The role works closely with Connect AI and other delivery teams to translate business needs into clear requirements, assess build, buy and reuse options, identify the best-fit delivery route and maintain focus on measurable outcomes. Technical design, engineering and solution ownership remain with the appropriate delivery teams.
The Consultant is responsible for rigorous analysis and accurate reporting at opportunity level, including financial and non-financial benefits, costs, assumptions, baselines, KPIs, benefit ownership, adoption and risk. Portfolio-level ownership and executive accountability remain with the AI Value Office Director and Head of AI Value Office.
Key Accountabilities
- Build trusted relationships with stakeholders to understand strategic priorities, challenges and opportunities.
- Identify, assess and prioritize AI opportunities based on business value, strategic alignment, readiness, risk and feasibility.
- Develop financial and non-financial business cases, including benefits, costs, KPIs, assumptions and adoption measures.
- Partner with Finance and business teams to validate value assumptions and track benefits.
- Work with AI, Technology, Data and Architecture teams to shape solutions and assess build, buy and reuse options.
- Define success measures and track initiatives from identified value through to realized outcomes.
- Provide clear portfolio reporting, insights and recommendations to senior stakeholders.
- Identify risks, dependencies, value leakage and opportunities to scale successful solutions.
- Ensure opportunities meet relevant governance, Responsible AI, risk and investment requirements.
- Contribute to improving Value Office tools, methodologies and ways of working.
Qualifications
- Experience in management consulting, internal consulting, business analysis, digital transformation, technology strategy, analytics or AI advisory.
- Proven experience framing complex business problems, capturing requirements and translating them into structured opportunities and recommendations.
- Experience developing business cases, investment proposals, benefits frameworks, KPIs and value-realization plans.
- Experience working across business, Finance, technology, data, risk and delivery teams in a matrixed organization.
- Strong track record of preparing and delivering clear presentations, reports and recommendations for senior stakeholders.
- Ability to work independently, manage multiple priorities and influence without direct authority.
- Experience in utilities, energy or another complex, regulated or asset-intensive industry is desirable.
Knowledge & Expertise
- Good understanding of analytics, artificial intelligence, machine learning, generative AI, automation and agentic AI, and how they can support business objectives.
- Understanding of the AI lifecycle from discovery and validation through build, deployment, adoption and value realization.
- Business analysis, process analysis, opportunity assessment and requirements-shaping methods.
- Financial and non-financial value assessment, including costs, benefits, ROI, total cost of ownership, assumptions, baselines and KPIs.
- Awareness of build, buy, configure and reuse considerations and different delivery models.
- Working knowledge of data readiness, enterprise architecture, information security, Responsible AI and technology risk.
- Sufficient technical literacy to challenge assumptions and work effectively with specialists, without being a hands-on data scientist, engineer or solution architect.
Salary
Brooklyn: $164k - $192k a year
Waltham: $153k - $180k a year
Syracuse: $136k - $160k a year
National Grid will not be providing visa sponsorship for this position now or in the future. You must have the ability to work without a need for current or future visa sponsorship.
National Grid utilizes an assessment that evaluates the job qualifications/characteristics using AI or statistically based scoring.
This position has a career path which provides for advancement opportunities within and across bands as you develop and evolve in the position; gaining experience, expertise and acquiring and applying technical skills. Candidates will be assessed and provided offers against the minimum qualifications of this role and their individual experience.
National Grid is committed to providing equal employment opportunities to all employees and applicants for employment regardless of protected class. We do not discriminate on the basis of race, color, religion, sex (including pregnancy, gender identity, and sexual orientation), national origin, age, disability, genetic information, or any other protected status in accordance with applicable federal, state, and local laws. National Grid maintains affirmative action programs for individuals with disabilities and protected veterans.
Our employment practices are designed to ensure that all individuals are treated fairly and with respect throughout the hiring process and during employment. National Grid complies with all applicable federal, state, and local anti-discrimination laws. We are dedicated to fostering a workplace that is free from unlawful discrimination and harassment, and we encourage a culture of respect for all.
Salary Context
This $164K-$192K range is above the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At National Grid, 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($178K) sits 16% below the category median. Disclosed range: $164K to $192K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
National Grid AI Hiring
National Grid has 3 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Warwick, England, UK, Brooklyn, NY, US, Brooklyn, NY. Compensation range: $192K - $192K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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.