Machine Learning Engineer- World-Leading Prop Trading Fund

Oxford Knight
London
8 months ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer (NLP)

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning EngineerSummary:

Exciting opportunity to work at a tech-centric prop trading fund which trades a wide range of financial products, with offices across the globe. Looking for an experienced ML Engineer with strong mathematical foundations to join their growing ML team and help drive the direction of the ML platform.

In this role, you'll draw on your in-depth knowledge of the ML ecosystem and understanding of varying approaches - whether it's neural networks, random forests, gradient-boosted trees, or sophisticated ensemble methods - to aid decision-making, choosing the right tool for the problem. Your work will also focus on enhancing research workflows to tighten feedback cycles. Successful ML engineers will be able to understand the mechanics behind various modeling techniques, while also being able to break down the mathematics behind them.

The successful candidate will be passionate about the craft of software engineering, who enjoys designing APIs systems that colleagues love to use. If you also have a great appetite for learning new things, this role is for you!

Requirements:

A strong mathematical background, in addition to experience with ML techniques and infrastructure You have previously built and maintained training and inference infrastructure A robust understanding of what it takes to move from concept to production Strong experience in model training & mathematical concepts, linear algebra, greek alphabet, choice of loss functions, regularization techniques, model architecture, optimizer, learning rate schedules, etc. Thorough understanding of Python tools and libraries, keen to offer advice on best practices Experience using Jax, Tensorflow or similar ML frameworks a huge plus


Benefits:
Market-leading salaries Generous benefits package, including physical & mental health benefits, excellent holiday entitlement, significant parental leave, retirement benefits, private on-site gym Focus on learning & development with tuition reimbursement Recreation spaces with breakfast, lunch, snacks and treats

Contact
If you feel you are a good match, please don't hesitate to get in touch:

Dan Hampton


linkedin/in/dan-hampton-ab029392

Job ID 75a0BqJh43nG

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Tips for Staying Inspired: How Machine Learning Pros Fuel Creativity and Innovation

Machine learning (ML) continues to reshape industries—from personalised e-commerce recommendations and autonomous vehicles to advanced healthcare diagnostics and predictive maintenance in manufacturing. Yet behind every revolutionary model lies a challenging and sometimes repetitive process: data cleaning, hyperparameter tuning, infrastructure management, stakeholder communications, and constant performance monitoring. It’s no wonder many ML professionals can experience creative fatigue or get stuck in the daily grind. So, how do machine learning experts keep their spark alive and continually generate fresh ideas? Below, you’ll find ten actionable strategies that successful ML engineers, data scientists, and research scientists use to stay innovative and push boundaries. Whether you’re an experienced practitioner or just breaking into the field, these tips can help you fuel creativity and discover new angles for solving complex problems.

Top 10 Machine Learning Career Myths Debunked: Key Facts for Aspiring Professionals

Machine learning (ML) has become one of the hottest fields in technology—touching everything from recommendation engines and self-driving cars to language translation and healthcare diagnostics. The immense potential of ML, combined with attractive compensation packages and high-profile success stories, has spurred countless professionals and students to explore this career path. Yet, despite the boom in demand and innovation, machine learning is not exempt from myths and misconceptions. At MachineLearningJobs.co.uk, we’ve had front-row seats to the real-life career journeys and hiring needs in this field. We see, time and again, that outdated assumptions—like needing a PhD from a top university or that ML is purely about deep neural networks—can mislead new entrants and even deter seasoned professionals from making a successful transition. If you’re curious about a career in machine learning or looking to take your existing ML expertise to the next level, this article is for you. Below, we debunk 10 of the most persistent myths about machine learning careers and offer a clear-eyed view of the essential skills, opportunities, and realistic paths forward. By the end, you’ll be better equipped to make informed decisions about your future in this dynamic and rewarding domain.

Global vs. Local: Comparing the UK Machine Learning Job Market to International Landscapes

How to evaluate opportunities, salaries, and work culture in machine learning across the UK, the US, Europe, and Asia Machine learning (ML) has rapidly transcended the research labs of academia to become a foundational pillar of modern technology. From recommendation engines and autonomous vehicles to fraud detection and personalised healthcare, machine learning techniques are increasingly ubiquitous, transforming how organisations operate. This surge in applications has fuelled an extraordinary global demand for ML professionals—data scientists, ML engineers, research scientists, and more. In this article, we’ll examine how the UK machine learning job market compares to prominent international hubs, including the United States, Europe, and Asia. We’ll explore hiring trends, salary ranges, workplace cultures, and the nuances of remote and overseas roles. Whether you’re a fresh graduate aiming to break into the field, a software engineer with an ML specialisation, or a seasoned professional seeking your next challenge, understanding the global ML landscape is essential for making an informed career move. By the end of this overview, you’ll be equipped with insights into which regions offer the best blend of salaries, work-life balance, and cutting-edge projects—plus practical tips on how to succeed in a domain that’s constantly evolving. Let’s dive in.