A Junior Research Engineer in Multimodal Large Language Models (LLMs)

The Machine Translation (MT) Unit pursues research in the field of automatic translation, both from text and speech, to enable multilingual communication across modalities. Its core activities include the creation of advanced models and their adaptation to diverse contexts, domains, and applications (e.g. simultaneous speech translation and subtitling), leveraging the latest technology, including LLMs and foundation models. The unit also conducts fundamental and applied research that focuses on the ethical and societal impact of translation technology, with a long history in the design of evaluation methods, resources and studies that include human participants (e.g. gender inclusive and human-centered AI). Job Description The successful candidate is expected to develop advanced models for multimodal (speech and video) processing, with a particular focus on fine-tuning strategies, integrating background knowledge (e.g., documents from knowledge bases), integrating speech and video modalities into LLMs, and optimization for computational efficiency. The candidate will also contribute to the evaluation and integration of these solutions in real-world use cases. Tasks Collect, filter, and process data, including speech and video; Develop and optimize algorithms and models for various real-world applications; Implement best practices in model development, validation, testing, and deployment to ensure high-quality and scalable solutions; Collaborate with team members to integrate research outcomes into practical solutions. Job Requirements The ideal candidate should have: Master’s degree in areas related to Computer Science and Engineering; Excellent programming skills (Python); Proven understanding of deep learning tools such as PyTorch; Good knowledge and proficiency in the English language; Team-oriented with excellent communication and interpersonal skills; Self-motivated and results driven. Preferred experience Ability to create, train, and optimize neural network architectures for multimodal (e.g., audio and/or video) signal processing; Experience with training and evaluating LLMs; Publication(s) in top AI conferences/journals are a plus. Employment Type of contract: fixed-term contract Working hours: Full time (38 h per week) Start date: February 2026 Duration: 24 months with the possibility of extending the contract depending on funding Workplace: Povo, Trento Gross annual salary: €38,539.67, plus objectives achievements bonus Benefits: flexi-time, company subsidized cafeteria or meal vouchers, internal car park, welcome office support for visa formalities and for research in accommodation, accommodation etc., supplementary pension and health fund, social security (SANIFONDS), family-work balance, free training courses, support on bank account opening, discount on public transport, sport, language course fees, counseling and psychological support service. More info at https://www.fbk.eu/en/work-with-us/ Application Interested candidates are requested to submit their application by completing the online form (https://jobs.fbk.eu/). Please make sure that your application contains the following attachments: Detailed CV including relevant past experiences (as an attached document in PDF format); Cover Letter (explaining your motivation for this specific position). Application deadline Wednesday, December 10th, 2025 J-18808-Ljbffr

Location: Trento, Trentino-Alto Adige, IT

Posted Date: 11/16/2025