Verified Global | AI Lead

Verified Global
London
4 months ago
Applications closed

Related Jobs

View all jobs

Data Analyst

Verified Global creates cutting-edge algorithms to turn the tables in sports betting. Every hour, millions of sports fans blow hard-earned cash on suboptimal, losing bets. We are turning the tables, arming the masses with market-beating tips, insights and data to unlock better bets and greater gains, all powered by world-class in-house algorithms.


All potential applicants are encouraged to scroll through and read the complete job description before applying.

Our flagship consumer product is a trailblazing platform leveraging our algorithmic and social expertise, unconstrained by any legacy engineering or design limitations. We have already experienced extremely rapid growth since launching in 2024, enabled by our industry-leading social media marketing channels where almost two million highly-engaged followers rely on our top-rated content every day.

Today, we are looking for a Lead AI Engineer to join us on this fast-moving and exciting journey.

Key responsibilities:

  • Lead the Charge in AI Innovation - Architect and develop groundbreaking AI models that push the boundaries of what's possible.
  • Design and develop cutting edge ML models for sports prediction. Improve the existing ensemble models for better predictability.
  • Bring the frontier of ML/AI research into sports by building self-learning and real-time predictive models for U.S. sports at a player-level resolution.
  • Optimise and bundle the ML models to serve thousands of predictions an hour. Currently our models run at blazing speeds on a Rust runtime through Onnx conversion.
  • Partnering with the product team to turn ideas into fully functional modules of the consumer product.
  • Contribute to strategic decisions around the future product direction (more powerful AI algorithms, valuable integrations and tools for sports fans).
  • Rapid testing and gathering of customer feedback and analytics to drive decision-making.

Desirable experience:

  • Deep understanding of machine learning fundamentals, enabling you to think creatively and solve complex problems.
  • Several years of hands-on experience building AI models.
  • Proficiency with Python.
  • Some experience with strongly-typed languages.
  • Proficiency with SQL-like databases and data pipelines.
  • Proficient understanding of dataframes, and ML frameworks including Scikit-learn, Tensorflow, PyTorch, Keras.
  • Previous experience with sports prediction, or interest in U.S. sports is desirable.
  • Some experience with Polars is desirable, though not necessary.
  • Some experience with Onnx is desirable, though not necessary.
  • Ability to work independently and in ambiguous settings, delivering solutions end-to-end.

What we offer:

  • Opportunity to directly impact and improve the decisions of millions of sports fans.
  • Unconstrained resources to experiment and train ML models on our cloud computing infrastructure.
  • Best in class real-time data through our direct contracts with data-providers.
  • A competitive salary and benefits package.
  • Private health insurance and fitness incentives.
  • Being part of the early founding team of an industry-first consumer product.
  • Truly intellectually challenging work in a diverse and fast-paced startup environment.
  • A talented and genuinely collegiate group of colleagues that are first-movers in their market.
  • Ample opportunity to grow and test your skills by working directly with top-tier ML, engineering, product, and design peers.
  • Commitment from us to continued investment in your professional development.

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.

Veterans in Machine Learning: A Military‑to‑Civilian Pathway into AI Careers

Introduction Artificial intelligence is no longer relegated to sci‑fi films—it underpins battlefield decision‑support, fraud detection, and even supermarket logistics. The UK Government’s 2025 AI Sector Deal forecasts an additional £200 billion in GDP by 2030, with machine‑learning (ML) engineers cited as the nation’s second most in‑demand tech role (Tech Nation 2024). The Ministry of Defence’s Defence AI Strategy echoes that urgency, earmarking £1.6 billion for FY 2025–28 to embed ML into planning, logistics, and autonomous systems. If you have ever tuned a radar filter, plotted artillery trajectories, or sifted sensor data for actionable intel, you have already worked with statistical modelling—the backbone of machine learning. This guide shows UK veterans how to reframe military experience for ML roles, leverage MoD transition funding, and land high‑impact positions building the models shaping tomorrow’s defence and commercial landscapes. Quick Win: Bookmark our live board for Machine‑Learning Engineer roles to see who’s hiring today.

Rural-Remote Machine Learning Jobs: Finding Balance Beyond the Big Cities

Over the past decade, machine learning (ML) has transformed from a niche research domain into a pervasive technology underpinning everything from recommendation systems and voice assistants to financial forecasting and autonomous vehicles. Historically, the UK’s major tech hubs—particularly London—have been magnets for top ML talent and corporate headquarters. However, remote work has become mainstream, and many ML professionals are realising they can excel in their field while living far beyond the city limits. At MachineLearningJobs.co.uk, we’ve observed a growing interest in positions that allow for a rural lifestyle or a coastal environment, often reflected in search terms like “ML remote countryside” or “tech jobs by the sea.” This surge is no coincidence. Flexible work policies, better rural broadband, and the nature of machine learning tasks—much of which can be done through cloud platforms—are bringing new opportunities to those who wish to swap urban hustle for fresh air and scenic views. Whether you’re a data scientist, ML engineer, researcher, or product manager, a rural or seaside move could reinvigorate your work-life balance. In this article, we’ll unpack why rural-remote ML jobs are on the rise, how you can navigate the challenges of leaving the city, and what you need to do to thrive in a machine learning career beyond the M25. If you’ve dreamt of looking up from your laptop to rolling fields or ocean waves, keep reading—your rural ML role might be closer than you think.

Quantum-Enhanced Machine Learning—Propelling AI into the Next Frontier

Machine learning (ML) has revolutionised how we interpret data, build predictive models, and create intelligent applications. From recommendation engines and self-driving cars to advanced genomics and natural language processing, ML solutions are integral to nearly every corner of modern life. However, as data complexity and model size continue to skyrocket, the computational demands placed on ML systems grow in tandem—often pushing even high-performance classical computers to their limits. In recent years, quantum computing has emerged as a tantalising solution to these challenges. Unlike traditional digital systems, quantum computers exploit quantum mechanics—superposition and entanglement—to process information in ways that defy conventional logic. As these machines mature, they promise exponential speed-ups for certain tasks, potentially reshaping how we approach AI and data-intensive challenges. What does this mean for machine learning? Enter quantum-enhanced ML, a new frontier where quantum processors and classical ML frameworks unite to accelerate model training, tackle high-dimensional data, and solve complex optimisation tasks more efficiently. In this article, we will: Unpack the current state of machine learning, highlighting key bottlenecks. Provide a concise overview of quantum computing—why it’s radical and how it differs from classical technology. Examine potential breakthroughs in quantum-enhanced ML, including real-world use cases and technical approaches. Explore the roles and skill sets that will define this quantum-AI era, with guidance on how to prepare. Discuss the roadblocks (like hardware maturity and ethical concerns) and how they might be addressed in the years to come. If you’re a machine learning engineer, data scientist, or simply an AI enthusiast fascinated by the next wave of computational innovation, read on—quantum computing could become an integral part of your future toolkit, opening up job opportunities and reimagining what ML can achieve.