Senior Engineer, Data Cloud

Senior Engineer – AI Data Generation & Optimization Research Join to apply for the Senior Engineer – AI Data Generation & Optimization Research role at Axelera AI Company Overview Axelera is a European, high‐growth Series B startup revolutionizing the AI landscape with our in‐memory computing platform. We specialize in creating AI hardware and software optimized for high‐performance inference, catering to cutting‐edge use cases across high‐end edge computing, embodied AI, and server‐side AI deployments. We are looking for passionate, innovative research engineers to join our team and help drive the future of AI. Role Overview We are seeking an AI Research Engineer with a strong focus on data generation, selection, and optimization . The role will be central to advancing our platform’s capabilities for generating high‐quality datasets, selecting the right data for training cutting‐edge AI models, and optimizing data pipelines to maximize the performance and scalability of our AI solutions. You will work closely with our AI researchers and engineers to create sustainable data processes that enable continuous improvement in our AI models, ensuring that our systems become smarter and more efficient over time. Your work will directly contribute to building a competitive edge for our products by harnessing the power of data. Responsibilities Data Generation: Develop innovative techniques for generating synthetic data or augmenting existing datasets to improve the quality, diversity, and representativeness of training data. Data Selection & Optimization: Design and implement data selection strategies that optimize model performance by choosing the most relevant data for training and validation. Focus on balancing the quality, diversity, and quantity of data to create more accurate models. Data Flywheel Platform: Build and optimize systems that create a self‐sustaining data generation and selection loop, allowing the company to continuously collect, process, and refine data for better AI model performance. Collaboration: Work closely with cross‐functional teams, including AI researchers, hardware engineers, and software engineers to integrate AI models into the broader platform. Innovation: Stay up‐to‐date with the latest research in multimodal AI, proposing and implementing new techniques to push the boundaries of what’s possible in generative AI. Requirements Experience: Proven experience (for all levels) in developing and optimizing data pipelines for machine learning, with a focus on data generation, data augmentation, or data selection techniques. Experience with data‐centric AI approaches, including data cleaning, augmentation, and synthetic data generation. Strong programming skills in Python and experience with data manipulation libraries (NumPy, Pandas) and nearest‐neighbour search libraries (FAISS). Knowledge of optimization techniques for large‐scale data pipelines, data storage, and distributed computing. Knowledge: A strong understanding of the latest advancements in AI/ML research, particularly in data optimization (e.g., task‐specific data selection, data pruning). Strong analytical skills to identify and implement strategies that enhance data quality and improve model performance. Preferred Qualifications PhD or advanced degree in Computer Science, Machine Learning, AI, or related fields. 5 years of post‐graduation relevant work experience. Experience building or working with large‐scale data systems or platforms (e.g., distributed data pipelines, cloud‐based data storage). Familiarity with reinforcement learning or generative modeling techniques, particularly for data generation. A passion for data‐driven approaches to AI development, and the ability to translate theoretical research into actionable, scalable solutions. Location This position is based in Italy and supports relocation to Bologna, Florence, or Milan for talent based abroad and interested in this role. Why Join Us Impact: Play a key role in shaping the future of AI by developing the foundational data systems that drive innovation and optimize AI models at scale.