683 python English-speaking jobs in Germany

environments Track record of shipping ML systems that deliver measurable business impact Strong proficiency in Python

Python, bash, REST-APIs) Experience in Git, Jira Basic knowledge of SQL, ideally also in PowerBI, MS

Python, bash, REST-APIs) Experience in Git, Jira Basic knowledge of SQL, ideally also in PowerBI, MS

At least 5 years of professional software engineering experience, with strong hands‐on Python skills.

problems, design, experiment, and implement solutions in programming languages such as C/C++, Java, Python

Very good knowledge in MS-Office Knowledge of programming languages and operating systems (C, C++, Python

Proficiency in Python and the scientific computing stack (PyTorch, NumPy, SciPy).

publications at top-tier peer-reviewed conferences or journals - Experience programming in Java, C++, Python

Knowledge of additional programming languages (e.g., Python, VHDL).

work autonomously, setting priorities and delivering to schedule  Competency in scripting languages (Python

Experience with scripting languages (Python, shell, etc.) for test automation and data analysis.

Have extensive experience with scripting languages such as Python, MATLAB/Octave or FORTRAN.

R or Python) would be an asset Very good level of written and spoken English, additional languages (especially

R or Python) would be an asset Very good level of written and spoken English, additional languages (especially

frameworks (e.g., LangChain or LlamaIndex), and Vector databases and embedding models Expert level in Python

runtime, and AI software ecosystems, including LLVM/MLIR, Triton, CUTLASS-style DSLs, PyTorch, JAX, Python

Embedded Fundamentals: Fluency in C/C++ on resource-constrained microcontrollers; proficiency with Python

Strong scripting skills (Bash, Python, etc.) and familiarity with Linux/Unix environments.

Several years of experience in machine learning engineering with Python (PyTorch) and strong software

. ~ Python, C# or TypeScript, REST and event-driven systems, observability practices, and experience