Research Engineer, Data (Foundational Research, Machine Learning)

NLP PEOPLE
London
1 week ago
Applications closed

Related Jobs

View all jobs

Sustainability Data Engineer

Python Data Engineer & Data Scientist

Machine Learning / Computer Vision Engineer – Data Scientist

AI Engineer (Machine Learning & Computer Vision)

C++ Software Engineer

Senior Electronic Design Engineer

Are you a curious and open-minded individual with an interest in state-of-the-art machine learning engineering and research? Thomson Reuters Labs is seeking a Data Engineer with a passion for solving challenging machine learning problems in a data-rich, complex and innovative environment.

What does Thomson Reuters Labs do? We experiment, we build, we deliver. We support the organization and our product teams through foundational research and development of new products and technologies. The Labs innovate collaboratively across our core segments in Legal, Tax & Accounting, Government, and Reuters News.

As a Research Engineer, you will be part of a diverse global team of experts. We hire world-leading specialists in SWE /Applied ML, as well as Research, to drive the company’s leading internal AI model development, fueled by an unprecedented wealth of data and powered by cutting-edge technical infrastructure.

About the Role:

In this opportunity as a Research Engineer – Data, you will:

  1. Innovate:Work at the cutting edge of AI Research, making the best use of rich data sources.
  2. Engineer and Develop:Design, develop, and optimize scalable data pipelines to support LLM training and evaluation.
  3. Collaborate:Work on a collaborative global team of engineers and scientists both within Thomson Reuters and our academic partners.

About You:

You’re a fit for the role of Research Engineer – Data, if your background includes:

Required qualifications:

  1. Relevant degree in a technical discipline.
  2. Interest in & experience working with (applied) machine learning.
  3. Excellent programming, debugging and system design skills.
  4. Excellent communication skills.
  5. Curious and innovative disposition.
  6. Self-driven attitude.
  7. Experience with relational and NoSQL databases.
  8. Experience with data pipeline orchestration tools.
  9. Experience with cloud-based data platforms.
  10. Comfortable working in fast-paced, agile environments.

Preferred qualifications:

  1. Additional legal knowledge.
  2. Ability to communicate with multiple stakeholders.
  3. Experience with big data technologies.
  4. Experience conducting world-leading research.
  5. Previous experience working on large-scale data processing systems.
  6. Strong software and/or infrastructure engineering skills.

What’s in it For You?

Join us to inform the way forward with the latest AI solutions and address real-world challenges in legal, tax, compliance, and news. This includes:

  1. Industry-Leading Benefits.
  2. Flexibility & Work-Life Balance.
  3. Career Development and Growth.
  4. Culture of inclusion, innovation, and customer-focus.
  5. Hybrid Work Model.
  6. Social Impact initiatives.

Do you want to be part of a team helping re-invent the way knowledge professionals work? Join us and help shape the industries that move society forward.

Accessibility

As a global business, we seek talented, qualified employees in all our operations around the world. Thomson Reuters is proud to be an Equal Employment Opportunity/Affirmative Action Employer providing a drug-free workplace.

#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.

Careers

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.

Careers

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.

Careers

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.