Research Scientist (m/f/x)

At ZEISS Corporate Research & Technology, we work at the frontier of science and technology to identify emerging computing architectures and translate them into future technology opportunities for ZEISS.
We are looking for a Research Scientist who understands accelerator architectures and fundamentally new computing concepts driven by the latest AI developments – from CPUs, GPUs and TPUs to custom accelerators, optical and analog computing, novel memory architectures and new forms of heterogeneous computing.
The position combines strategic technology exploration with hands-on technical work. You will build targeted proof-of-concepts to validate promising architecture concepts, while engaging with startups, research groups and technology partners to understand emerging solutions first-hand, assess their maturity and identify the most promising approaches for ZEISS
Your role
Analyze AI computing systems end-to-end: Understand how training and inference workloads run on modern data-center architectures and identify the fundamental bottlenecks in compute, memory, interconnects and energy efficiency.
Explore next-generation computing architectures: Evaluate established, emerging and fundamentally new concepts – custom accelerators, chiplets, in- and near-memory computing, optical and analog computing and heterogeneous systems.
Build and benchmark proof-of-concepts: Translate architectural hypotheses into hands-on demonstrators and assess their potential quantitatively against the state of the art.
Scout and recommend: Engage with startups, research groups and technology providers, judge maturity and differentiation, and distinguish fundamental technology shifts from incremental improvements.
Your profile
A university degree in computer science, computer engineering, electrical engineering with a focus on digital systems or computer architecture, or a closely related discipline. A PhD or equivalent research experience is welcome.
Expertise in at least two of the following areas: computer architecture and systems; machine-learning systems and AI infrastructure; heterogeneous accelerators and emerging computing architectures.
A systems-level understanding of modern AI technology stacks – training and inference workflows, numerical kernels, memory hierarchies, data movement, interconnects, accelerator hardware and system software – and the ability to explain where bottlenecks occur, why, and which architectural approaches can address them.
Programming skills in Python and/or C/C++; experience with GPU computing, parallel programming, distributed systems, compilers, runtimes or HPC environments is an advantage.
Curiosity and openness toward external technology developments, and the ease to engage directly with startups, research groups and technology providers, understand their approaches in technical depth and assess their relevance.
An analytical eye for technology trends, the flexibility to revise your view when the evidence changes, and the ability to present complex topics clearly. Business-fluent English is required.
Your ZEISS Recruiting Team:
Friederike Kirklar-Harms