C++ Quant Developer - Systematic Equities | London- Leading Multi-Strategy IM

Oxford Knight
London
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Machine Learning Engineer - Research

Senior Data Engineer (2 days onsite in London)

Data Engineer - Azure

Lead Data Engineer

2 x Data Analyst - Local Authority

Data Engineer

C++ Quant Developer - Systematic Equities | LondonSalary:£150-350k TC

Summary

Superb opportunity to join one of the world's most prestigious hedge funds as a Quant Developer within Systematic Equities. This is a high impact role, within a small, entrepreneurial investment team, where you will be building critical trading infrastructure in a highly collaborative environment.

Working directly with the senior PM and quant researchers, your primary focus will be designing, coding, and maintaining tools for the systematic trading infrastructure. You'll develop data engineering and prediction tools for the systematic trading of equities, implement technology to enable large-scaleputational efforts in quant research, and build and maintain robust data pipelines and databases.

To succeed in this role, you will have exceptionalmunication skills,fortable facing off to the business, with a real drive for collaborative success.

Skills and Experience Required

5+ years' experience with a strongputer science or engineering background Expert-level C++ programming experience, plus advanced Python Track record in Linux-based development Experience with DevOps functions ( Google Cloud, Airflow, InfluxDB, Grafana) Degree (Masters or PhD preferred) inputer Science, Physics, Engineering, Statistics, Applied Mathematics, or related technical field, from a top-tier university


Desirable
Knowledge of machine learning and statistical techniques and related libraries Experience as a quantitative developer supporting an intraday (or faster) system Experience with the development practices of large tech (Google/Meta, etc.) or finance firms
Benefits & Incentives:
Significant salary + bonus + benefits Dynamic, fast-paced environment; excellent career growth opportunities Collaborative culture and an energetic, dynamic engineering atmosphere Build and share knowledge with the smartest engineers in the industry

Contact
If you think you are a good fit for the role and would like further information, please contact:

Dominic Copsey

+44 (0) 203 475 7193
linkedin/in/dom-copsey-586478143/

Job ID z0NdBEIkGpAD

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.