Staff Engineer AMS Implementation (f/m/div)

Stellenbeschreibung:

#WeAreIn for jobs that impact everyone's life. What if your ideas could change the way the world connects, powers up, or thinks? As a Staff Engineer AMS Implementation (f/m/div) on our Research & Development team, you'll have the opportunity to merge creativity with your technical expertise by shaping the future of technology, driving groundbreaking projects, and bringing new ideas to life. Are you in?

Your Role

Develop software and algorithms for the automated physical design of analog integrated circuits as well as discrete power devices and charge‑coupled devices.
Build and optimize spatial indexing and graph‑based engines: sweep‑line operations, interval/segment trees, R‑tree‑based querying, planar graph construction and analysis, and matching algorithms for constraints like symmetry and alignment.
Model complex AMS layout rules via constraint programming, implement and tune Gecode or OR‑Tools CP‑SAT models; develop custom propagators where needed, establish diagnostics and KPIs for model quality and solver performance.
Parallelize compute‑intensive kernels using OpenMP and standard parallel algorithms; optimize memory behavior and validate scalability on Linux/LSF clusters.
Establish testing and quality practices for geometry kernels and CP models: create unit tests and fuzzing for geometric edge cases.
Improve existing software modules including refactoring, testing and maintenance of existing code.
Communicate and collaborate with our international analog design automation team to guarantee the success of all the above activities.

Your Profile

A degree in Electrical Engineering, Computer Science or similar; Doctorate (Ph.D.) strongly preferred.
Minimum 3–5 years of algorithm development experience directly applicable to AMS‑EDA, especially computational geometry for analog layout synthesis, constraint programming and solver use (e.g., Gecode, OR‑Tools CP‑SAT) and CPU‑only parallel algorithms in modern C++ using OpenMP and standard parallel algorithms (C++17/C++20).
Deep knowledge of spatial and graph data structures (e.g., sweep‑line, interval/segment trees, R‑trees, planar graphs, matching/flow) relevant to analog physical design.
Advanced knowledge of Python GUI programming and Cadence SKILL, as well as knowledge of the Cadence Virtuoso software suite (schematic and layout entry), is required.
Experience in quantum computing, machine learning, artificial neural networks and reinforcement learning, as well as associated software such as model development in Python/Torch.
Familiarity with the Unix/Linux operating system as a user and administrator, as well as experience in shell scripting and the use of LSF server farms.
Fluent English language skills; German is a plus.