Machine Learning Engineer

Oho Group Ltd
Edinburgh
1 month ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer

Machine Learning Engineer (NLP)

Machine Learning Engineer - Defence Sector - Cambridge

Machine Learning / Computer Vision Engineer – Data Scientist

Lead Machine Learning Engineer

Senior Machine Learning Engineer

Machine Learning Engineer - Stealth-Mode Startup | Edinburgh | Research-Focused


A unique opportunity has arisen for a talented machine learning engineer to join a stealth-mode startup based in Edinburgh. Focused on applied machine learning and advanced statistical modeling, this role offers the chance to be part of a founding team shaping the future of a groundbreaking company.


The Opportunity

As a Machine Learning Engineer you will work at the forefront of Bayesian modeling, tackling complex problems across diverse industries. They will lead the development of cutting-edge probabilistic models, contribute to experimental design optimisation, and collaborate on transformative research projects.


The ideal candidate would have:

  • 2+ years experience as a Machine Learning Engineer
  • A degree / PhD from a top university in a relevant field (bonus points if it was heavily statistics focussed)
  • Expertise in Bayesian statistics and a passion for innovation.
  • You thrive in a research-intensive environment and are excited by the challenge of building technology in uncharted domains
  • You're a curious mindset, strong technical expertise, and the ability to work autonomously are essential.


Why Join?

This is a rare chance to be part of a stealth-mode startup with a vision to revolutionise experimentation. Based in Edinburgh, the company offers the excitement of an early-stage venture, the opportunity to shape its culture and direction, and the chance to create meaningful global impact.


For those ready to push the boundaries of applied machine learning, this role promises unparalleled challenges and rewards!


Machine Learning Engineer - Stealth-Mode Startup | Edinburgh | Research-Focused

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.

Tips for Staying Inspired: How Data Science Pros Fuel Creativity and Innovation

Data science sits at the dynamic intersection of statistics, computer science, and domain expertise, driving powerful innovations in industries ranging from healthcare to finance, and from retail to robotics. Yet, the daily reality for many data scientists can be a far cry from starry-eyed talk of AI and machine learning transformations. Instead, it often involves endless data wrangling, model tuning, and scrutiny over metrics. Maintaining a sense of creativity in this environment can be an uphill battle. So, how do successful data scientists continue to dream big and innovate, even when dealing with the nitty-gritty of data pipelines, debugging code, or explaining results to stakeholders? Below, we outline ten practical strategies to help data analysts, machine learning engineers, and research scientists stay inspired and push their ideas further. Whether you’re just starting out or looking to reinvigorate a long-standing career, these pointers can help you find fresh sparks of motivation.

Top 10 Data Science Career Myths Debunked: Key Facts for Aspiring Professionals

Data science has become one of the most sought-after fields in the tech world, promising attractive salaries, cutting-edge projects, and the opportunity to shape decision-making in virtually every industry. From e-commerce recommendation engines to AI-powered medical diagnostics, data scientists are the force behind innovations that drive productivity and improve people’s lives. Yet, despite the demand and glamour often associated with this discipline, data science is also shrouded in misconceptions. Some believe you need a PhD in mathematics or statistics; others assume data science is exclusively about machine learning or coding. At DataScience-Jobs.co.uk, we’ve encountered a wide array of myths that can discourage talented individuals or mislead those exploring a data science career. This article aims to bust the top 10 data science career myths—providing clarity on what data scientists actually do and illuminating the true diversity and inclusiveness of this exciting field. Whether you’re a recent graduate, a professional looking to pivot, or simply curious about data science, read on to discover the reality behind the myths.

Global vs. Local: Comparing the UK Data Science Job Market to International Landscapes

How to evaluate salaries, opportunities, and work culture in data science across the UK, the US, Europe, and Asia Data science has proven to be more than a passing trend; it is now a foundational pillar of modern decision-making in virtually every industry—from healthcare and finance to retail and entertainment. As the volume of data grows exponentially, organisations urgently need professionals who can transform raw information into actionable insights. This high demand has sparked a wave of new opportunities for data scientists worldwide. In this article, we’ll compare the UK data science job market to those in the United States, Europe, and Asia. We’ll explore hiring trends, salary benchmarks, and cultural nuances to help you decide whether to focus your career locally or consider opportunities overseas or in fully remote roles. Whether you’re a fresh graduate looking for your first data science position, an experienced data professional pivoting from analytics, or a software engineer eager to break into machine learning, understanding the global data science landscape can be a game-changer. By the end of this overview, you’ll be better equipped to navigate the expanding world of data science—knowing which skills and certifications matter most, how salaries differ between regions, and what to expect from distinct work cultures. Let’s dive in.