Research Engineer / Research Scientist

Berg Search
2 months ago
Applications closed

Related Jobs

View all jobs

ML/AI Software Engineer

Senior AI Researcher

Connectomics Data Analysis Engineer

Principal Data Scientist - NLP

Machine Learning / Computer Vision Engineer – Data Scientist

Sustainability Data Engineer

We are working with an exciting client at the forefront of innovation in the intelligence operating system space. They are transforming how organizations deliver large-scale change by addressing the challenges of project complexity, delays, and cost overruns!


Role Overview

As a Research Engineer / Research Scientist in the Graph Knowledge Base team, you will lead the research, development, and optimisation of the company's knowledge graph infrastructure. Your work will advance our GraphRAG pipelines, semantic understanding techniques, and NLP capabilities, ensuring that enterprises can effortlessly discover, organise, and utilise their data. You’ll enjoy the freedom to experiment with emerging methodologies, collaborate with cross-disciplinary teams, and see your innovations drive measurable improvements in real-time decision-making.


Key Responsibilities

  • Innovate and Shape: Own end-to-end R&D of advanced knowledge graph systems, setting newstandardsfor programme intelligence.
  • Deliver Impact: Connect technical innovation to tangible results, enhancing how enterprises retrieve, integrate, and leverage data to inform critical decisions.
  • Advance GraphRAG Workflows: Optimise retrieval-augmented generation processes for low-latency, high-accuracy semantic data integration.
  • Integrate Unstructured Data: Collaborate with data engineers and scientists to transform a broad spectrum of unstructured textual content into a unified, queryable knowledge base.
  • Stay Ahead of the Curve: Research and implement the latest advances in knowledge representation, semantic search, and AI-driven retrieval to continuously refine our platform’s capabilities.
  • Safeguard Security and Compliance: Ensure all solutions adhere to data security, privacy standards, and role-based access controls, maintaining strict compliance with regulations.
  • Validate and Optimise: Conduct thorough testing and validation to guarantee accuracy, scalability, and reliability of knowledge graph systems.
  • Communicate and Collaborate: Present findings, prototypes, and technical solutions to stakeholders, embracing feedback and refining outcomes through iterative improvement.


Expertise and Skills

Core Technical Competencies:

  • Knowledge Graphs & AI-driven Knowledge Representation: Deep understanding of designing, optimising, and integrating knowledge graphs with advanced AI-based techniques (e.g., vector embeddings, transformer-based models) for semantic retrieval.
  • GraphRAG and AI/ML: Practical experience with Retrieval-Augmented Generation (RAG) and frameworks like LangChain or LangGraph, plus comfort with cutting-edge NLP techniques.
  • Programming & Prototyping: Advanced proficiency in Python for rapid prototyping, experimentation, and implementation.

Data Infrastructure & Tooling:

  • Database & Graph Databases: Strong theoretical and practical grasp of data structures, algorithms, and graph databases (e.g., Neo4j, Cosmos DB).
  • Cloud Ecosystems: Experience deploying secure, scalable AI solutions in cloud environments such as Microsoft Azure, AWS, or GCP.

Security & Compliance:

  • Standards and Best Practices: Familiarity with data security frameworks and compliance standards, integrating these into R&D workflows.

Nice to Have:

  • Deep Learning & Scientific Computing: Experience with frameworks like PyTorch, TensorFlow, or JAX, as well as libraries such as NumPy or SciPy, to support advanced experimentation and model fine-tuning.

Mindset & Approach:

  • Innovative and Inquisitive: A passion for pushing boundaries, independently exploring new ideas, and staying at the forefront of AI and programme intelligence.
  • Ownership and Accountability: Proven track record of taking projects from concept to successful implementation, thriving in dynamic, evolving environments.
  • Collaboration and Communication: Comfortable working closely with data engineers, data scientists, and domain experts, contributing to a culture of shared learning and continuous improvement.


What Success Looks Like

Success in this role will be measured by notable improvements in data retrieval speed, insight accuracy, user satisfaction metrics, and overall platform stability. Your innovations will directly influence the strategic direction of the company's AI capabilities, ensuring that global enterprises have the intelligence they need at their fingertips.



What We Offer

  • Competitive salary
  • Bonus scheme
  • Wellness allowance
  • Fully remote working (with regular company get-togethers)
  • Private medical and dental insurance*
  • Life assurance, critical illness cover, and income protection*

*Provision and availability depend on your country of residence – we’ll discuss this with you.

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 Machine Learning Pros Fuel Creativity and Innovation

Machine learning (ML) continues to reshape industries—from personalised e-commerce recommendations and autonomous vehicles to advanced healthcare diagnostics and predictive maintenance in manufacturing. Yet behind every revolutionary model lies a challenging and sometimes repetitive process: data cleaning, hyperparameter tuning, infrastructure management, stakeholder communications, and constant performance monitoring. It’s no wonder many ML professionals can experience creative fatigue or get stuck in the daily grind. So, how do machine learning experts keep their spark alive and continually generate fresh ideas? Below, you’ll find ten actionable strategies that successful ML engineers, data scientists, and research scientists use to stay innovative and push boundaries. Whether you’re an experienced practitioner or just breaking into the field, these tips can help you fuel creativity and discover new angles for solving complex problems.

Top 10 Machine Learning Career Myths Debunked: Key Facts for Aspiring Professionals

Machine learning (ML) has become one of the hottest fields in technology—touching everything from recommendation engines and self-driving cars to language translation and healthcare diagnostics. The immense potential of ML, combined with attractive compensation packages and high-profile success stories, has spurred countless professionals and students to explore this career path. Yet, despite the boom in demand and innovation, machine learning is not exempt from myths and misconceptions. At MachineLearningJobs.co.uk, we’ve had front-row seats to the real-life career journeys and hiring needs in this field. We see, time and again, that outdated assumptions—like needing a PhD from a top university or that ML is purely about deep neural networks—can mislead new entrants and even deter seasoned professionals from making a successful transition. If you’re curious about a career in machine learning or looking to take your existing ML expertise to the next level, this article is for you. Below, we debunk 10 of the most persistent myths about machine learning careers and offer a clear-eyed view of the essential skills, opportunities, and realistic paths forward. By the end, you’ll be better equipped to make informed decisions about your future in this dynamic and rewarding domain.

Global vs. Local: Comparing the UK Machine Learning Job Market to International Landscapes

How to evaluate opportunities, salaries, and work culture in machine learning across the UK, the US, Europe, and Asia Machine learning (ML) has rapidly transcended the research labs of academia to become a foundational pillar of modern technology. From recommendation engines and autonomous vehicles to fraud detection and personalised healthcare, machine learning techniques are increasingly ubiquitous, transforming how organisations operate. This surge in applications has fuelled an extraordinary global demand for ML professionals—data scientists, ML engineers, research scientists, and more. In this article, we’ll examine how the UK machine learning job market compares to prominent international hubs, including the United States, Europe, and Asia. We’ll explore hiring trends, salary ranges, workplace cultures, and the nuances of remote and overseas roles. Whether you’re a fresh graduate aiming to break into the field, a software engineer with an ML specialisation, or a seasoned professional seeking your next challenge, understanding the global ML landscape is essential for making an informed career move. By the end of this overview, you’ll be equipped with insights into which regions offer the best blend of salaries, work-life balance, and cutting-edge projects—plus practical tips on how to succeed in a domain that’s constantly evolving. Let’s dive in.