Research Engineer / Research Scientist

Berg Search
1 year ago
Applications closed

Related Jobs

View all jobs

NLP/LLM Research Scientist (PhD) – Hybrid, Cambridge

NLP/LLM Research Scientist (PhD) – Cambridge Hybrid

Research Engineer, Machine Learning - Paris/London/Zurich/Warsaw

Machine Learning Software Engineer, Research

Machine Learning Engineer/Researcher

Machine Learning Engineer/Researcher

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.

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.

Careers Jobs

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

Jobs

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Education Jobs

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

🍪 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.