Research Engineer - Information Retrieval and Generative Models
Location - 100% On-site in Amsterdam, Netherlands
We are seeking a highly skilled and motivated Research Engineer to focus on information retrieval and generative models. This role involves developing, implementing, and optimizing models to improve the accuracy and efficiency of information retrieval and enhance user experiences with generative models. A key area of interest is multimodality, where inputs extend beyond text. You will collaborate with researchers, data scientists, and engineers to create innovative solutions leveraging large-scale datasets and advanced machine learning techniques.
Key Responsibilities
- Design, implement, and optimize information retrieval models, establishing robust retrieval-augmented generation (RAG) systems.
- Improve large language models (LLMs) by enhancing reasoning capabilities and reducing hallucinations.
- Conduct research on cutting-edge methods in natural language processing (NLP), machine learning (ML), and information retrieval (IR).
- Develop algorithms and techniques to boost the performance and scalability of RAG systems.
- Collaborate with cross-functional teams to integrate RAG solutions into existing products and services.
- Perform data collection, preprocessing, and analysis to support R&D activities.
- Evaluate and benchmark the performance of RAG models on large-scale datasets.
- Stay informed about the latest advancements in AI, NLP, ML, and IR.
Qualifications
- Master's or Ph.D. in Computer Science, Electrical Engineering, or a related field, focusing on NLP, ML, or IR.
- Strong programming skills in Python and familiarity with machine learning frameworks such as PyTorch.
- Solid understanding of deep learning architectures, including transformers (e.g., BERT, GPT).
- Proven research excellence demonstrated through publications in relevant conferences or journals.
- Ability to work both independently and collaboratively in a team environment.
- Excellent problem-solving skills with attention to detail.
- Strong written and verbal communication skills.
Preferred Qualifications
- Experience with automatic speech recognition (ASR) systems.
- Hands-on knowledge of retrieval-based models and techniques such as vector space models, BM25, or neural retrieval methods.
- Familiarity with knowledge bases, information extraction, and question-answering systems.
- Background in software development and engineering best practices.