Share this job!

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.

• 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
  

• 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 Model Context Protocol (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
  

Get to know us!