Lead Machine Learning Engineer

National Grid plc
Warwick
10 months ago
Applications closed

Related Jobs

View all jobs

Lead Machine Learning Engineer

Lead Machine Learning Engineer (Sports Insights)

Lead Machine Learning Engineer (Sports Insights)

Lead Software Engineer - Agentic AI/Machine Learning

Machine Learning Engineer - Tech Lead

Machine Learning Engineering Lead

Location:London, GB, WC2N 5EH; Warwick, GB, CV34 6DA

Company:National Grid

At National Grid, we keep people connected and society moving. But it’s so much more than that. National Grid supplies us with the environment to make it happen. As we generate momentum in the energy transition for all, we don’t plan on leaving any of our customers in the dark. So, join us as a Lead Machine Learning Engineer, and find your superpower.

Job Overview:National Grid is hiring a Lead Machine Learning Engineer for our IT & Digital department. This is a hybrid role that offers flexible working options and will require occasional visits to Warwick or London.

As a Lead Machine Learning Engineer on the National Grid Data Science team, you will develop data pipelines, take data science prototype models to production, fix production bugs, monitor operations, and provision the necessary infrastructure in Azure.

Key Accountabilities

  • Develop platform tooling (e.g., internal conda library, CLI tool for project setup, and provisioning infrastructure) for the Data Science team.
  • Work with data scientists to understand their data needs and put together data pipelines to ingest data.
  • Work with data scientists to take data science model prototypes to production.
  • Mentor and train junior team members.
  • Work with internal IT teams (security, Cloud, Global Active Directory, Architecture, Networking, etc.) to advance the team’s projects.
  • Design, provision, and maintain the cloud infrastructure needed to support Data Engineering, Data Science, Machine Learning Engineers, and Machine Learning Operations.
  • Write high-quality code that has high test coverage.

About You

  • Hands-on industry experience in some combination of Software Engineering, ML Engineering, Data Science, DevOps, and Cloud Infrastructure work.
  • Expertise in Python which includes experience in libraries such as Pandas, scikit-learn. High proficiency in SQL.
  • Knowledge of best practices in software engineering is necessary.
  • Hands-on industry experience in some combination of the following technologies: Python ecosystem, Azure (VMs, Web Apps, Managed Databases), GitHub Actions, Terraform, Packer, Airflow, Docker, Kubernetes, Linux/Windows VM administration, Shell scripting (primary Bash but PowerShell as well).
  • A solid understanding of modern security and networking principles and standards.
  • A foundational knowledge of Data Science is strongly preferred.
  • Bachelor’s or higher degree in Computer Science, Data Science, and/or related quantitative degree is preferred from an accredited institution.

More Information

A salary between £80,000 – £95,000 – dependent on capability.

As well as your base salary, you will receive a bonus of up to 15% of your salary for stretch performance and a competitive contributory pension scheme where we will double match your contribution to a maximum company contribution of 12%. You will also have access to a number of flexible benefits such as a share incentive plan, salary sacrifice car and technology schemes, support via employee assistance lines and matched charity giving to name a few.

Inclusivity Statement:At National Grid, we work towards the highest standards in everything we do, including how we support, value and develop our people. Our aim is to encourage and support employees to thrive and be the best they can be. We celebrate the difference people can bring into our organisation, and welcome and encourage applicants with diverse experiences and backgrounds, and offer flexible and tailored support, at home and in the office.

Our goal is to drive, develop and operate our business in a way that results in a more inclusive culture. All employment is decided on the basis of qualifications, the innovation from diverse teams & perspectives, and business need. We are committed to building a workforce so we can represent the communities we serve and have a working environment in which each individual feels valued, respected, fairly treated, and able to reach their full potential.

#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.