Machine Learning Engineer with Data Engineering expertise

Tadaweb
London
2 months ago
Applications closed

Related Jobs

View all jobs

Staff Software Engineer, MLOps (Remote within UK)

Machine Learning Engineer( Real time Data Science Applications)

Machine Learning Engineer

Lead Data Engineer - MLOps

Data Engineer | Various Levels | Competitive package

Data Engineer

Machine Learning Engineer with Data Engineering expertise

Tadaweb

is a pioneering technology company with roots in Luxembourg and a growing global presence, with offices in the United Kingdom, France, and the United States. For over 13 years, we’ve been on a mission to make the world a safer place by empowering analysts with the tools they need to access the right information at the right time. Our cutting-edge SaaS platform revolutionizes PAI and OSINT investigations, making them faster, smarter, and more effective, all while adhering to the highest ethical standards by relying solely on publicly available information and supporting our clients’ policies. Renowned for our “nothing is impossible” ethos, we prioritize trust, transparency, and innovation in everything we do.

Remember to check your CV before applying Also, ensure you read through all the requirements related to this role.About the Role:We are looking for a

Machine Learning Engineer

with Data Engineering expertise to help scale our platform. In this hybrid role, you’ll design data pipelines, develop ML models, and work across data and AI systems to enhance our platform’s capabilities. If you thrive in a collaborative, fast-moving environment and want to make a real-world impact, we’d love to hear from you!Scope of Work:Machine Learning EngineeringDesign, develop, evaluate, and deploy machine learning models for production.Optimize model performance based on key metrics for scalability, reliability, and real-world impact.Build and maintain end-to-end ML pipelines, including data preprocessing, model training, deployment, and monitoring.Work closely with cross-functional teams to integrate ML models into our SaaS platform for PAI and OSINT investigations.Develop, maintain, and optimize scalable data pipelines for ingesting, processing, and storing large volumes of data.Ensure data quality, consistency, and availability to support ML workflows.Work with ELT processes and implement Medallion (Bronze/Silver/Gold) architecture to structure and optimize data transformation.Align data infrastructure with business needs and product strategy for PAI and OSINT.System Optimization & SupportMonitor, test, and troubleshoot data and ML systems for performance improvements.Recommend and implement enhancements to data pipelines, ML workflows, and system reliability.Ensure seamless integration of new ML models and data-driven features into production.Your Profile:Experience in both data engineering and machine learning, with a strong portfolio of relevant projects.Proficiency in Python with libraries like TensorFlow, PyTorch, or Scikit-learn for ML, and Pandas, PySpark, or similar for data processing.Experience designing and orchestrating data pipelines with tools like Apache Airflow, Spark, or Kafka.Strong understanding of SQL, NoSQL, and data modeling.Familiarity with cloud platforms (AWS, Azure, GCP) for deploying ML and data solutions.Knowledge of MLOps practices and tools, such as MLflow or Kubeflow.Strong problem-solving skills, with the ability to troubleshoot both ML models and data systems.A collaborative mindset and ability to work in a fast-paced, small team environment.You get bonus points if you have any of the following:Experience working with geospatial data or network graph analysis.Familiarity with PAI and OSINT tools and methodologies.Hands-on experience with containerization technologies like Docker.Understanding of ethical considerations in AI, data privacy, and responsible machine learning.Our Offer:The opportunity to join a growing tech company, with strong product-market fit and an ambitious roadmap.The chance to join a human-focused company that genuinely cares about its employees and core values.A focus on performance of the team, not hours at the desk.A social calendar including family parties, games nights, annual offsites, end of the year events and more, with an inclusive approach for both younger professionals and parents.Tadaweb is an equal opportunities employer, and we strive to have a team with diverse perspectives, experiences, and backgrounds.Our culture:Our company culture is driven by the core values of family first, nothing is impossible and work hard, play harder. We provide a healthy and positive culture that cares about employee wellbeing by creating a great workplace and investing in our employees' learning and development. Our leaders aspire to the philosophies of extreme ownership, and servant leadership.Seniority level:

Mid-Senior levelEmployment type:

Full-timeJob function:

Information TechnologyIndustries:

Software Development

#J-18808-Ljbffr

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.