sonarsource - Sonar - AI Research Scientist/Engineer
Requirements
• An advanced academic background (Master’s or PhD) in Computer Science, Machine Learning, or a related quantitative field. • Strong industry experience in machine learning, with a solid understanding of modern software engineering practices and tools. • Solid programming skills in Python and hands-on experience with core ML/DL frameworks (e.g., PyTorch, TensorFlow, Hugging Face). Familiarity with Java is a plus. • Proven experience in applied Machine Learning, with a strong focus on Natural Language Processing (NLP) or, ideally, Programming Language Processing (PLP). • Hands-on experience with modern LLM architectures and techniques, such as Fine-tuning strategies (e.g., LoRA, QLoRA), advanced prompt engineering, building and optimizing Retrieval-Augmented Generation (RAG) pipelines and working with vector databases and semantic search • Experience with large-scale data processing frameworks and cloud infrastructure (e.g. AWS). • Experience of driving research projects from initial ideation to a demonstrable prototype with a high degree of autonomy. • Excellent communication skills in English and a talent for explaining complex scientific topics clearly and concisely. • Additional comments • This role is based in Bochum. We are unable to consider candidates unwilling to be in Bochum, but we are willing to relocate the right candidate.
Responsibilities
• Spearhead Research & Innovation: Stay on the cutting edge of ML, Deep Learning, and LLMs, specifically their application to the Software Development Lifecycle (SDLC), and identify novel opportunities to enhance our products. • Develop Advanced AI Models: Design, prototype, and validate novel ML models that identify and resolve complex bugs, vulnerabilities, and code smells, going beyond the capabilities of traditional static analysis. • Build LLM-Powered Features: Develop and implement advanced LLM-based solutions, including Retrieval-Augmented Generation (RAG) for contextual code analysis, fine-tuning models on proprietary codebases, and exploring agentic systems for automated code remediation. • Engineer Data Pipelines: Build and manage robust data pipelines to gather, process, and version massive code-centric datasets required for training and evaluating specialized models at scale. • Translate Prototypes to Products: Collaborate closely with engineering and product teams to integrate successful ML prototypes into Sonar's cutting-edge products, ensuring they meet the needs of our global user base. • Communicate and Evangelize: Clearly articulate and document complex technical concepts and research findings to both technical and non-technical stakeholders.
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