Energy Management/Strategy Engineer - Formula E

MASERATI MSG RACING
Plymouth
10 months ago
Applications closed

Related Jobs

View all jobs

Data Analyst

Data Engineer (18 Months FTC)

Data Engineer (18 Months FTC)

Senior Data Engineer

SAP Master Data Analyst

Data Analyst - Power BI Specialist

Location:Monaco
Contract Type:Permanent
Experience Required:1-3 years in a similar position


Job Description:


As part of our commitment to Formula E, we are looking for a talented and analytical Energy Management Engineer to join our team. This is an office-based position where you will work closely with performance, race engineers, and simulation specialists to develop and optimize race strategies focused on energy efficiency and overall performance.


Main Responsibilities:


  • Develop, analyse, and optimize energy management strategies for race scenarios.
  • Use simulation tools and real-time data to predict and enhance energy deployment efficiency and thermal behaviour.
  • Liaise with head of strategy to translate simulations into track applications.
  • Optimize energy management and strategy based on track characteristics, weather conditions, and competitor behaviour.
  • Be involved in the Driver in the Loop simulator (DiL) and embedded software development
  • Contribute to driver briefings with data-driven recommendations for optimal energy usage and recovery.
  • Stay up to date with Formula E regulations and ensure compliance in all strategic approaches.

Profile Sought:


  • Engineering degree in automotive, mechanical, data science, or a related field.
  • 1-3 years’ experience in a high-level racing series (F1, FE, WEC, F2).
  • Strong understanding of energy management principles, battery systems, and efficiency optimisation.
  • Expertise in data analysis tools (MATLAB, Python, Excel, race simulation software).
  • Experience in laptime simulation softwares (preferably Canopy) is a plus
  • Ability to work effectively in an office-based environment with occasional travel for tests or race support.
  • Fluency in English required; French is a plus.
  • Strong analytical skills, attention to detail, and ability to work under pressure in a fast-paced environment.

Why Join Us?


  • Be part of a dynamic and innovative team in a rapidly growing championship.
  • Contribute to cutting-edge developments in energy management and race strategy.
  • Opportunities for career growth and continuous training in an international setting.

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.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

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.