Our team has an immediate permanent opening for a Research Engineer.
Responsibilities:
- Conduct advanced research to explore and apply state-of-the-art LLM and AI techniques to improve software engineering processes, including requirements analysis, system design, modelling, and automated software testing.
- Develop novel frameworks and methodologies for integrating LLMs into software engineering workflows. This includes applying prompt engineering, retrieval-augmented generation (RAG), self-consistency methods, reflection techniques, search and planning algorithms, and evaluation metrics to enhance system performance and decision-making.
- Design and implementation of techniques that combine symbolic reasoning with generative AI models, aiming to bridge the gap between data-driven and logic-based approaches to problem-solving in software systems.
- Collaborate with cross-functional teams of researchers, engineers, and product experts to integrate AI-driven solutions into real-world software systems engineering challenges. Communicate research findings through academic publications and industry reports.
- Stay at the forefront of LLM advancements and related AI technologies, identifying opportunities for innovation and contributing to the development of next-generation software systems engineering tools and techniques.
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Requirements
What you’ll bring to the team:
- A Ph.D. in Software Engineering, Requirements Engineering, Artificial Intelligence, Natural Language Processing (NLP), or closely related fields, with a focus on the application of Large Language Models and AI techniques.
- Research & development experience in the application of AI/LLMs in the software engineering domain, with a solid understanding of both theoretical foundations and practical implementations; Strong programming skills and experience in LLM development tools.
- Proven ability to address complex challenges in AI/LLM applications, particularly in integrating AI-driven insights into software engineering tasks such as requirement specification, system design, and quality assurance.
- Demonstrated ability to work effectively in interdisciplinary teams, with strong communication skills to convey complex technical concepts to non-expert stakeholders and present findings at conferences or workshops.