Research Engineer / Research Scientist

Berg Search
1 year ago
Applications closed

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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.

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