National AI Awards 2025Discover AI's trailblazers! Join us to celebrate innovation and nominate industry leaders.

Nominate & Attend

Data Engineer- AI SaaS

Vortexa
London
5 months ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Vortexa is a fast-growing international technology business founded to solve the immense information gap that exists in the energy industry. By using massive amounts of new satellite data and pioneering work in artificial intelligence, Vortexa creates an unprecedented view on the global seaborne energy flows in real-time, bringing transparency and efficiency to the energy markets and society as a whole.

http://www.vortexa.com/

Processing thousands of rich data points per second from many and vastly different external sources, moving terabytes of data while processing it in real-time, running complex prediction and forecasting AI models while coupling their output into a hybrid human-machine data refinement process and presenting the result through a nimble low-latency SaaS solution used by customers around the globe is no small feat of science and engineering. This processing requires models that can survive the scrutiny of industry experts, data analysts and traders, with the performance, stability, latency and agility a fast-moving startup influencing multi-$m transactions requires.

The Data Production Team is responsible for all of Vortexa’s data. It ranges from mixing raw satellite data from 600,000 vessels with rich but incomplete text data, to generating high-value forecasts such as the vessel destination, cargo onboard, ship-to-ship transfer detection, dark vessels, congestion, future prices, etc

The team has built a variety of procedural, statistical and machine learning models that enabled us to provide the most accurate and comprehensive view of energy flows. We take pride in applying cutting-edge research to real-world problems in a robust, long-lasting and maintainable way. The quality of our data is continuously benchmarked and assessed by experienced in-house market and data analysts to ensure the quality of our predictions.

You’ll be instrumental in designing and building infrastructure and applications to propel the design, deployment, and benchmarking of existing and new pipelines and ML models. Working with software and data engineers, data scientists and market analysts, you’ll help bridge the gap between scientific experiments and commercial products by ensuring 100% uptime and bulletproof fault-tolerance of every component of the team's data pipelines.

Requirements

You Are:

  • Experienced in building and deploying distributed scalable backend data processing pipelines that can go through terabytes of data daily using AWS, K8s, and Airflow.
  • With solid software engineering fundamentals, fluent in both Java and Python (with Rust good to have).
  • Knowledgeable about data lake systems like Athena, and big data storage formats like Parquet, HDF5, ORC, with a focus on data ingestion.
  • Driven by working in an intellectually engaging environment with the top minds in the industry, where constructive and friendly challenges and debates are encouraged, not avoided
  • Excited about working in a start-up environment: not afraid of challenges, excited to bring new ideas to production, and a positive can-do will-do person, not afraid to push the boundaries of your job role.
  • Passionate about coaching developers, helping them improve their skills and grow their careers
  • Deep experience of the full software development life cycle (SDLC), including technical design, coding standards, code review, source control, build, test, deploy, and operations

Awesome If You:

  • Have experience with Apache Kafka and streaming frameworks, e.g., Flink,
  • Familiar with observability principles such as logging, monitoring, and tracing
  • Have experience with web scraping technologies and information extraction.

Benefits

  • A vibrant, diverse company pushing ourselves and the technology to deliver beyond the cutting edge
  • A team of motivated characters and top minds striving to be the best at what we do at all times
  • Constantly learning and exploring new tools and technologies
  • Acting as company owners (all Vortexa staff have equity options)– in a business-savvy and responsible way
  • Motivated by being collaborative, working and achieving together
  • A flexible working policy- accommodating both remote & home working, with regular staff events
  • Private Health Insurance offered via Vitality to help you look after your physical health
  • Global Volunteering Policy to help you ‘do good’ and feel better
National AI Awards 2025

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.

How to Present Machine Learning Solutions to Non-Technical Audiences: A Public Speaking Guide for Job Seekers

Machine learning is driving change across nearly every industry—from retail and finance to health and logistics. But while the technology continues to evolve rapidly, the ability to communicate it clearly has become just as important as building the models themselves. Whether you're applying for a junior ML engineer role, a research position, or a client-facing AI consultant job, UK employers increasingly expect candidates to explain complex machine learning solutions to non-technical audiences. In this guide, you’ll learn how to confidently present your work, structure your message, use simple visuals, and explain the real-world value of machine learning in a way that makes sense to people without a background in data science.

Machine Learning Jobs UK 2025: 50 Companies Hiring Now

Bookmark this page—we refresh the Hotlist every quarter so you always know who’s really scaling their ML teams. The UK’s National AI Strategy, a £2 billion GenAI accelerator fund and a record flow of private capital have kicked ML hiring into overdrive for 2025. Whether you build production‑grade LLM services or optimise on‑device models for edge hardware, employers need your skills now. Below you’ll find 50 organisations that advertised UK‑based machine‑learning vacancies or announced head‑count growth during the past eight weeks. They’re grouped into five quick‑scan categories so you can jump straight to the type of employer—and mission—that excites you. For each company we list: Main UK hub Example live or recent vacancy Why it’s worth a look (stack, impact, culture) Search any employer on MachineLearningJobs.co.uk to see real‑time adverts, or set a free alert so fresh openings drop straight in your inbox.

Return-to-Work Pathways: Relaunch Your Machine Learning Career with Returnships, Flexible & Hybrid Roles

Returning to work after an extended break can feel like starting from scratch—especially in a specialist field like machine learning. Whether you paused your career for parenting, caring responsibilities or another life chapter, the UK’s machine learning sector now offers a variety of return-to-work pathways. From structured returnships to flexible and hybrid roles, these programmes recognise the transferable skills and resilience you’ve developed, pairing you with mentorship, upskilling and supportive networks to ease your transition back. In this guide, you’ll discover how to: Understand the current demand for machine learning talent in the UK Leverage your organisational, communication and analytical skills in ML contexts Overcome common re-entry challenges with practical solutions Refresh your technical knowledge through targeted learning Access returnship and re-entry programmes tailored to machine learning Find roles that fit around family commitments—whether flexible, hybrid or full-time Balance your career relaunch with caring responsibilities Master applications, interviews and networking specific to ML Learn from inspiring returner success stories Get answers to common questions in our FAQ section Whether you aim to return as an ML engineer, research scientist, MLOps specialist or data scientist with an ML focus, this article will map out the steps and resources you need to reignite your machine learning career.