Senior Machine Learning Engineer (m/f/d)

Drive innovation as a Senior Machine Learning Engineer (m/f/d) at momox SE in Berlin. Flexible hours and impactful work await you. As a Senior Machine Learning Engineer (m/f/d), you'll play a key role in designing, developing, and operationalizing machine learning models that drive core business areas such as pricing, logistics, marketing and customer experience. You'll join a team that values experimentation, pragmatic engineering, and end-to-end ownership - from research to production. Your mission Design and implement robust machine learning solutions for real-world applications at scale Take algorithms and models from research to production, ensuring reliability, scalability, and observability Build and maintain ML pipelines using modern MLOps tools (e.g., Kubeflow Pipelines, MLflow, Weights & Biases, Evidently AI) Deploy and serve models using frameworks like FastAPI, Docker and Kubernetes, ensuring efficient retraining and monitoring Collaborate cross-functionally with data scientists, engineers, and product managers to translate business needs into ML-driven solutions Contribute to the evolution of momox's ML system, and advocate for best practices in model lifecycle management and continuous improvement Our tech stack Python: PyTorch, FastAPI, Scikit-learn, HuggingFace, LitServe, vLLM Cloud & Infrastructure: Docker, Kubernetes, GCP CI/CD: Gitlab, ArgoCD Model orchestration: Kubeflow (Vertex AI pipelines) Data: BigQuery, Postgres, Milvus, FAISS, FEAST Monitoring: Grafana, Evidently AI, W&B, Prometheus Your profile Master's or PhD in Computer Science, Engineering, Mathematics, Statistics or a related field focused on Machine Learning 3-5+ years of experience developing and deploying ML systems in production environments Deep understanding of ML algorithms, statistical modeling, and deep learning techniques Proficiency with PyTorch and at least one other modern ML framework such as JAX or TensorFlow Solid experience with MLOps, model orchestration, and observability tools (e.g., Prometheus, CI/CD, MLflow) Experience working with cloud platforms (GCP, AWS or Azure) Strong communication skills - you can clearly explain complex ML concepts to both technical and non-technical audiences Fluency in English (German is a plus but not required) Nice to have: Hands-on experience with vision-language models, reinforcement learning or recommender systems Knowledge of distributed training and large-scale inference Familiarity with multi-component pipelines (MCP) and emerging agentic workflows Understanding of safe GenAI application design and evaluation Experience using vector databases (e.g., Milvus, Pinecone) for retrieval-augmented systems Domain experience in pricing, logistics or marketing ML applications

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