Machine Learning Engineer

Echobox
London
1 week ago
Create job alert
About Echobox:
We are a fast-growing, research-driven company building an artificial intelligence that helps online publishers overcome the challenges they face every day. Using novel AI, we are revolutionising the publishing industry and have a track record of building things that others have ruled out as impossible. Leading names from around the world rely on our product every day, including The Times, Le Monde, The Guardian, Vogue and many more.
Our team is our best asset. We work with extremely smart and talented individuals, who all enjoy a high degree of responsibility and independence in structuring their work.
Do you think you have what it takes to be part of Echobox? Wed love to hear from you.

About the Role:
You will report to our Head of Data Science and work closely with our Product managers, Software engineers and Data Scientists to define and execute on the future path for our products.

Key Responsibilities:
  1. Work closely with senior engineers and data scientists to quickly learn and apply machine learning techniques to real-world problems, shipping results fast, all whilst meeting launch deadlines.
  2. Take ownership of end-to-end ML model development—from data preprocessing and feature engineering to training, testing, and deployment.
  3. Collaborate across teams to implement machine learning solutions into production systems, ensuring that models are scalable, reliable, and effective.
  4. Actively contribute to refining and improving existing models and systems. If something can be optimized, youre on it—constantly looking for ways to enhance performance.
  5. Quickly analyze data and generate insights to drive product decisions. You’ll focus on making things work fast and efficiently, without over-complicating the process.
  6. Document your work and share findings clearly with the team. No jargon—just straightforward, actionable insights.
  7. Continuously learn new techniques and stay up to date with the latest ML trends, applying them to improve the product as you go.

Requirements:
  1. A degree in Computer Science, Data Science, or a related field (or equivalent practical experience).
  2. 2-3 years of experience in machine learning, with a strong understanding of core ML algorithms and frameworks (e.g., scikit-learn, TensorFlow, PyTorch).
  3. Hands-on experience with data preprocessing, feature engineering, and model training for real-world problems.
  4. Strong Python and Java programming skills and familiarity with NLP algorithms and libraries.
  5. Solid understanding of basic statistics and how to apply it to real-world machine learning tasks.
  6. Familiarity with cloud platforms (AWS) and Kubernetes for deploying and scaling models.
  7. A passion for solving problems with data and machine learning, always looking for ways to get things done quickly and effectively.
  8. A proactive, results-driven mindset—eager to take ownership of tasks and deliver value without waiting for direction.
  9. Ability to work independently, learn fast, and iterate without being bogged down by unnecessary processes.
  10. Fluent written and spoken English.

Preferred Requirements:
  1. Experience in a fast-paced SaaS or tech environment, with an emphasis on deploying ML models to production quickly.
  2. Knowledge of deep learning models and frameworks, and interest in exploring cutting-edge ML techniques.
  3. Experience working with large datasets and distributed computing environments.
  4. Excellent organisational, analytical and influencing skills, with proven ability to take initiative and build strong, productive relationships.
  5. Experience working with cross-functional teams within a software organisation.
  6. Be able to easily switch between thinking creatively and analytically.
  7. An interest in the future of the publishing industry.

Benefits:
Our employees enjoy free breakfast every day, coffee, drinks and snacks all day, everyday. Every Monday and Friday, we order food for our weekly team lunches where everyone gets together for an hour of fun. We have regular team events (dinner, bowling, karting, poker nights, board-games etc.) for our team to get to know each other outside of work. Professionally, we host in-house conferences and an annual summer camp for all our global employees who are flown to and hosted in London. We ensure that all our employees also get pension contributions, the latest tech, generous annual leave and an amazing office with a balcony overlooking Notting Hill.

J-18808-Ljbffr

Related Jobs

View all jobs

Machine Learning Engineer( Real time Data Science Applications)

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

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.