AWM - London - Vice President - Software Engineering London · United Kingdom · Vice President

Goldman Sachs Bank AG
London
11 months ago
Applications closed

AWM - London - Vice President - Software Engineering location_on London, Greater London, England, United Kingdom

Opportunity Overview

CORPORATE TITLE: Vice President
OFFICE LOCATION(S): London
JOB FUNCTION: Software Engineering
DIVISION: Asset & Wealth Management

What We Do

At Goldman Sachs, our Engineers don’t just make things – we make things possible. Change the world by connecting people and capital with ideas. Solve the most challenging and pressing engineering problems for our clients. Join our engineering teams that build massively scalable software and systems, architect low latency infrastructure solutions, proactively guard against cyber threats, and leverage machine learning alongside financial engineering to continuously turn data into action. Create new businesses, transform finance, and explore a world of opportunity at the speed of markets.

Engineering, which is comprised of our Technology Division and global strategists’ groups, is at the critical center of our business, and our dynamic environment requires innovative strategic thinking and immediate, real solutions. Want to push the limit of digital possibilities? Start here.

A career with Goldman Sachs is an opportunity to help clients across the globe realize their potential, while you discover your own. As part of one of the world’s leading asset managers with over $2 trillion in assets under supervision, you can expect to participate in exciting investment opportunities while collaborating with talented colleagues from all asset classes and regions and building meaningful relationships with your clients. Working in a culture that values integrity and transparency, you will be part of a diverse team that is passionate about our craft, our clients, and building sustainable success.

Who We Look For

Goldman Sachs Engineers are innovators and problem-solvers, building solutions in risk management, big data, mobile and more. We look for creative collaborators who evolve, adapt to change and thrive in a fast-paced global environment.

HOW YOU WILL FULFILL YOUR POTENTIAL

  • Be a major contributor to the build out of the ETF platform, including taking projects from beginning to end, from analysis, design, implementation, and go-live
  • Work with portfolio manager, traders, and operations to understand requirements for new ETF products, as well as to identify opportunities for efficiency improvements
  • Support product launches and ongoing ETF operations

SKILLS AND EXPERIENCE WE ARE LOOKING FOR

  • 5+ years of experience as a Software Engineer
  • A degree in Computer Science or related field
  • Experience with back-end service development in Java
  • Experience successfully collaborating directly with stakeholders to understand the product space, identify solutions, and finally deliver software products
  • Knowledge of asset management, particularly Equities, Fixed Income and ETFs is a big plus
  • Comfort with multi-tasking, a fast-paced environment, and managing multiple stakeholders
  • Experience working as part of a global team
  • Excellent written and spoken communication

Goldman Sachs is an equal employment/affirmative action employer Female/Minority/Disability/Veteran/Sexual Orientation/Gender Identity.

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.

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.