Machine Learning Engineer

Tadaweb
London
3 months ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer (Forward Deployed)

Machine Learning Engineer

Machine Learning Engineer / MLOps Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

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.


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 Engineering

  • Develop, maintain, and optimize scalable data pipelines & machine learning models 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.

Data Engineering

  • 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 & Support

  • Monitor, 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.
  • Track record of delivering end-to-end ML solutions integrated into SaaS products
  • 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.
  • Experience with CI/CD for ML and data workflows
  • 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 our employees learning and development. Our leaders aspire to the philosophies of extreme ownership, and servant leadership.

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.

New Machine Learning Employers to Watch in 2026: UK and Global Companies Driving ML Innovation

Machine learning (ML) has transitioned from a specialised field into a core business capability. In 2026, organisations across healthcare, finance, robotics, autonomous systems, natural language processing, and analytics are expanding their machine learning teams to build scalable intelligent products and services. For professionals exploring opportunities on www.MachineLearningJobs.co.uk , understanding the companies that are scaling, winning investment, or securing high‑impact contracts is crucial. This article highlights the new and high‑growth machine learning employers to watch in 2026, focusing on UK innovators, international firms with significant UK presence, and global platforms investing in machine learning talent locally.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

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.