AI researcher

AI workloadresearcher Company

Imec aims to be the world-leading R&D and innovation hub in nanoelectronics and digital technologies. As a trusted partner for companies, startups and academia we bring together brilliant minds from all over the world in a creative and stimulating environment. By leveraging our world-class infrastructure and local and global ecosystem of diverse partners across a multitude of industries, we are accelerating progress towards a connected, sustainable future.

Within the imec ISSA expertise center, we build application-oriented prototypes around unique imec chip-level technology. Together with the component experts, we innovate at the system level to bring these imec technologies to the next level and to bridge the gap between research and industry. We define the next generation of architectural system blueprints for hardware and software. Our mission is to demonstrate and validate these systems in relevant (real life) application environments.

As a workload researcher, you combine skills of different trades such as system architecture, functional analysis, data science, statistics and synthetic data generation with a key focus on data-compute interactions. You’ll focus on how systems become more autonomous and how that evolution shapes their architectural needs over the next five to ten years. You’ll bridge software and hardware gaps and guide the evolution of distributed compute architectures through analysis of dominant workloads.

Core

Activities:

• You explore novel algorithms / optimizations for (hyper distributed) architectures to enable SW-HW codesign and help shape our activities around this.
• Analyze and identify dominant workloads and their evolution as systems become more autonomous.
• Generate, analyze and type AI, hybrid-AI and synthetic workloads, including their performance behaviour, task graphs, and execution patterns across local and hyper distributed systems.
• Bridge the gap between software and hardware architectures, using workload insights to develop the design of future distributed compute systems.
• You test and validate the performance of mini-applications on both existing hardware and simulated novel hardware and use the outcomes for further hardware/software explorations.

We offer you the opportunity to join one of the world’s premier research centers in nanotechnology at its headquarters in Leuven, Belgium. With your talent, passion and expertise, you’ll become part of a team that makes the impossible possible. Together, we shape the technology that will determine the society of tomorrow.

We are committed to being an inclusive employer and proud of our open, multicultural, and informal working environment with ample possibilities to take initiative and show responsibility. We commit to supporting and guiding you in this process; not only with words but also with tangible actions. Through imec.academy, 'our corporate university', we actively invest in your development to further your technical and personal growth.

We are aware that your valuable contribution makes imec a top player in its field. Your energy and commitment are therefore appreciated by means of a market appropriate salary with many fringe benefits.

• Master’s degree or PhD in Computer Science, Artificial Intelligence, Systems Engineering or equivalent.
• Think system‑level and enjoy analyzing complex systems‑of‑systems.
• Be curious about how autonomy, AI, and distributed (agentic) systems will evolve over the next 5–10 years.
• Feel comfortable operating between conceptual architecture and hands‑on engineering.
• Enjoy breaking down complex applications into workloads, task graphs, and execution patterns.
• Naturally bridge software and hardware perspectives.
• Like experimenting, prototyping, and turning ideas into working code.
• Be comfortable collaborating closely with software developers, researchers, and architects.
• Knowledge of most frequently used frameworks:
• Agentic components and standards (e.g. LLMs, MCP, Lang Chain, RAG).
• Data science formats and tools (e.g. Parquet, JSON, Python libs, …).
• Statistical methods to generate synthetic data (e.g. Bayesian, ARIMA, KNN,…