Machine Learning Manager

Deliveroo
London
2 weeks ago
Create job alert

Machine Learning Engineering ManagerLondonAbout the RoleAt Deliveroo we have an outstanding data science organisation, with a mission to enable the highest quality human and machine decision-making. We work throughout the company - in product, business and platform teams - using analysis, experimentation, causal inference and machine learning techniques. We are uniquely placed to use data to help make better decisions and improve data literacy across Deliveroo.Machine Learning (ML) Engineers work in cross-functional teams of engineers, data scientists, and product managers to build the algorithmic products that power the company. We are embedded in product teams, close to the business problems and go after some of the hardest problems. ML Engineers translate a fuzzy business problem to a concrete pipeline that we design and implement. We then work closely with the engineers to deploy our models to production and with data scientists to run experiments based on these algorithms.ML Engineers at Deliveroo report into our Science management team, and we have a strong, active data science community with guest lecturers, a robust technical review process, a career progression framework, and plenty of opportunities to learn new things. We have career pathways for both managers and individual contributors. Our ML Engineers come from many disciplines but have excellence in common. Many are formally trained in Machine Learning, many are not.We are looking for a Machine Learning Engineering Manager to join our management team and lead our Search & Relevance team. This team optimises the customer experience algorithmically, mainly through recommendation engines and search & ranking algorithms. The team currently has a mix of MLEs of differing levels of seniority, including mid-level, Senior and Staff.Ideal candidates will: * Have experience line-managing machine learning engineers and guiding their career development. * Have built and deployed machine learning algorithms to production within product teams. * Provide technical guidance and input on the design and implementation of machine learning algorithms. * Have experience working with cross-functional teams and managing stakeholders throughout the business, helping them to identify opportunities and build roadmaps. * Bring together a group of individuals from many different backgrounds and skill sets to form a cohesive team. * Be comfortable working in an extremely fast, constantly changing environment. * Have a pragmatic, flexible approach, and most cares about achieving impact * [bonus] Knowledge and experience with experimentationWorkplace & BenefitsAt Deliveroo we know that people are the heart of the business and we prioritise their welfare. Benefits differ by country, but we offer many benefits in areas including healthcare, well-being, parental leave, pensions, and generous annual leave allowances, including time off to support a charitable cause of your choice. Benefits are country-specific, please ask your recruiter for more information.DiversityAt Deliveroo, we believe a great workplace is one that represents the world we live in and how beautifully diverse it can be. That means we have no judgement when it comes to any one of the things that make you who you are - your gender, race, sexuality, religion or a secret aversion to coriander. All you need is a passion for (most) food and a desire to be part of one of the fastest-growing businesses in a rapidly growing industry.We are committed to diversity, equity and inclusion in all aspects of our hiring process. We recognise that some candidates may require adjustments to apply for a position or fairly participate in the interview process. If you require any adjustments, please don't hesitate to let us know. We will make every effort to provide the necessary adjustments to ensure you have an equitable opportunity to succeed

Related Jobs

View all jobs

Machine Learning Manager, London

Machine Learning Engineering Manager

Senior Machine Learning Product Manager (Deploy)

Machine Learning Engineer

Machine Learning Performance Engineer

Machine Learning Engineer - Personalisation

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.

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.