—
Role: ML Software Engineer Location: Munich, Germany Hybrid: 2 days a week from the office. Job Description :: In your role you will support the development of internal ML tooling and working towards productization of prototypic workflows into compliant ML products at scale. - Support development of internal ML tooling o Develop novel, or improve existing tools o Add new features, fix bugs, raise test coverage and improve documentation - Ensure compliance of processes and developed tooling Must-have Skills or Experiences - Experience in building and applying ML (not hosted LLM) systems with frameworks such as PyTorch, TensorFlow, JAX - Experience in building training data processing pipelines for image data - Experience in in MLOps lifecycles (model training, model validation, model deployment, quality monitoring) *Though we would also consider similar experiences to the above, for instance building data processing pipelines for proteomics data instead of image data, etc. - Python (excellent, required) - Software engineering best practices, the aim of writing high performant, modern, bug free code, proper documentation of code, unit-testing, code quality, etc. (required) - PyTorch (required good working knowledge) Desirable Skills or Experiences - TypeScript/Node/JavaScript (desirable) - Experiment tracking platforms such as Weights & Biases, MLFlow (past experiences) - Processing platforms, such as Kubeflow (optional) - Computer Vision problems (recommended), e.g. experience with OpenCV (optional) - Cloud computing infrastructure, in particular AWS (recommended) - Working in a regulated environment, developing code under pre-set regulatory rules (nice to have)
| Skill | Source | Confidence |
|---|---|---|
| MLOps | llm_hard |
100%
|
| Python | llm_hard |
100%
|
| PyTorch | llm_hard |
100%
|
| Data Pipelines | llm_hard |
100%
|
| TensorFlow | llm_hard |
100%
|
| Model Deployment | llm_hard |
100%
|
| AWS (SageMaker, EC2, S3) | llm_hard |
80%
|
| Computer Vision | llm_hard |
80%
|
| Skill | Source | Confidence |
|---|---|---|
| Documentation | llm_soft |
100%
|
| Technical Writing | llm_soft |
80%
|
| Query | Country | Status | Response ms | Created |
|---|---|---|---|---|
| ML Software Engineer | extracted | 3925 | 2026-03-22 03:59 | |
| ML Software Engineer | classified | 529 | 2026-03-21 21:58 | |
| junior programmer in Munich | de | processed | 11664 | 2026-03-21 17:51 |
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"job_description": "Role: ML Software Engineer\n\nLocation: Munich, Germany\n\nHybrid: 2 days a week from the office.\n\nJob Description ::\n\nIn your role you will support the development of internal ML tooling and working towards productization of prototypic workflows into compliant ML products at scale. - Support development of internal ML tooling o Develop novel, or improve existing tools o Add new features, fix bugs, raise test coverage and improve documentation - Ensure compliance of processes and developed tooling\n\nMust-have Skills or Experiences\n\n- Experience in building and applying ML (not hosted LLM) systems with frameworks such as PyTorch, TensorFlow, JAX\n\n- Experience in building training data processing pipelines for image data\n\n- Experience in in MLOps lifecycles (model training, model validation, model deployment, quality monitoring) *Though we would also consider similar experiences to the above, for instance building data processing pipelines for proteomics data instead of image data, etc.\n\n- Python (excellent, required) - Software engineering best practices, the aim of writing high performant, modern, bug free code, proper documentation of code, unit-testing, code quality, etc. (required)\n\n- PyTorch (required good working knowledge)\n\nDesirable Skills or Experiences\n\n- TypeScript/Node/JavaScript (desirable)\n\n- Experiment tracking platforms such as Weights & Biases, MLFlow (past experiences)\n\n- Processing platforms, such as Kubeflow (optional)\n\n- Computer Vision problems (recommended), e.g. experience with OpenCV (optional)\n\n- Cloud computing infrastructure, in particular AWS (recommended)\n\n- Working in a regulated environment, developing code under pre-set regulatory rules (nice to have)",
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