Man Technology Summer Internship - 2025

Man Group plc
London
1 year ago
Applications closed

Related Jobs

View all jobs

Data Engineer (T-SQL, Python)

Data Science Lead

About the Programme

Successful candidates will spend 12 weeks fully embedded within one of our teams focused on a self-contained project designed to drive technological advancement and make a significant impact on our business. Each intern will receive comprehensive support including an assigned a buddy and mentor.

The projects will span across a multitude of technologies such as software development, devops and data engineering. Throughout the programme you will build an understanding of our business and the financial markets working closely with areas such as Portfolio Management, Trading, Risk, Responsible Investment, Operations and many many more. Towards the end of the 12 weeks, you will have the opportunity to present your project to the Technology Management Committee.

When you are not working on your project, you will have plenty of time to attend talks, training and social events as well as utilising our office amenities including a Music Room, Makerspace and Gadget Lending Library.

Candidates who perform strongly during the Internship Programme may be offered a full-time position on the Technology Foundation Programme.

Requirements:

  • A geniune passion for technology and strong programming skills. Please include links to any relevant content (e.g. github, involvement in previous Internship Programmes or Hackathons) on your application.
  • Experience working with any of the following programing languages : Python, Java, C#.Net or C++ or demonstrable understanding of how an enterprise infrastructure functions.
  • Currently studying for a degree in Computer Science, Mathematics, Engineering, Physics or a related technical discipline (desired but not necessary).
  • Candidates should be in the penultimate year of their master's or bachelor's degree.

Application Process

  • Application Window:30th September - 15th November 2024
  • CV Screen:Once we have reviewed your CV you will hear from us by email to confirm whether your application is being progressed. We do receive a high volume of applications so there may be a wait to hear back but we will be in touch as soon as possible.
  • Online Test:The next stage will be a technical homework. This should take no longer than two hours for you to complete.
  • Technical Interview;There will be a 1 hour technical interview with an engineer from the team to assess your coding skills.
  • Final Interview:You will spend 30 minutes with one of our tech managers as a final stage to assess your suitability for the programme, either at our office in London, or we can complete this via VC.

Programme Start Date: June 2025

About Us

Man Group is a global, technology-empowered active investment management firm focused on delivering alpha and portfolio solutions for clients. Technology and data powers everything we do at Man Group. We believe Man Technology is the place to be if you want to work where forward-thinking, open, collaborative technology meets investment management.

Our teams ensure the firm has a robust tech platform that facilitates alpha generation, portfolio management, trade execution, operations, compliance, risk management and accounting, as well as providing firm-wide end user collaboration tools. Our platform is built to handle scale, complexity and customisation.

We are actively engaged with the broader technology community.

  • We host and sponsor London's PyData meetups, Machine Learning meetups and DEV.BG community events.
  • We open-source some of our technology. See github.com/man-group.
  • We regularly talk at leading industry conferences

Our Culture, Values and Benefits at Man

Man Group is proud to provide the best working environment possible for all of its employees, and we are committed to equal opportunities. At Man Group we believe that a diverse workforce is a critical factor in the success of our business and this is embedded in our culture and values. There are a number of external and internal initiatives, partnerships and programmes that help us to attract and develop talent from diverse backgrounds and that encourage inclusion and diversity across our firm and the industry. Man Group is a Signatory of the Women in Finance Charter and the Race at Work Charter.

Man Group supports many charities, and global initiatives. We support professional training and development, and requests for flexible or part-time working. Employees are also offered two 'Mankind' days of paid leave per year as part of the Man Charitable Trust's community volunteering programme.

We offer comprehensive, firm-wide employee benefits including competitive holiday entitlements, pension/401k, life and long-term disability coverage, group sick pay, enhanced parental leave and long-service leave. Additional benefits are tailored to local markets and may include private medical coverage, discounted gym membership and wellbeing programmes.

Man Group is a Disability Confident Committed employer; if you require help or information on reasonable adjustments as you apply for roles with us, please contact .

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.