Machine Learning Engineer – (Revenue Management & Price Optimization) (mfd)

Join our team of machine learning experts to develop and implement cutting-edge models specifically tailored for price optimization. You will work closely with data scientists to deploy low latency services in production with the objective to infer optimal bid price leveraging theories of opportunity cost. Your work will directly influence pricing strategies for millions of customers.

YOUR ROLE AT SIXT

• You will develop regression prediction model to: optimal prices under capacity constraints ensuring that architectures effectively capture demand elasticity capacity constraints and temporal stationary context
• You will prototype and evaluate novel architectures: including deep MLPs transformer-based models for tabular or sequence data CNNs for image inputs and embedding-rich hybrid models all optimized for accuracy robustness interpretability and transfer learning derived from pooling information and maximizing out-of-sample generalization
• You will conduct large-scale offline experimentation and simulation to assess model generalization and study model behaviour across geographies seasons and capacity constraints
• You will work with Large-Scale Model Training and Optimization: Design train and optimize large neural network architectures using distributed data processing frameworks such as PySpark Dask or Polars. Apply multi-GPU and multi-node parallelization to accelerate deep model training and inference at scale
• You will work with distributed ML Infrastructure: Architect and maintain distributed training environments enabling efficient training across clusters of GPUs. Ensure model reproducibility fault tolerance and cost-efficient scaling on AWS
• You will  work with Data Engineering for Scale: Develop high-performance ETL and feature engineering pipelines using Polars Dask or Spark optimized for terabyte-scale datasets. Implement columnar storage memory mapping and data sharding strategies to enable efficient GPU utilization
• You will  work with Monitoring and Continuous Optimization: Continuously monitor large-scale model performance using distributed logging and metric systems (e.g. Prometheus Grafana MLflow). Automate feedback loops retraining triggers and hyperparameter tuning with Ray Tune or Optuna to maintain adaptive performance under changing market dynamics.
• You will  have Knowledge Sharing and Leadership: Document infrastructure design scaling strategies and distributed optimization techniques. Mentor peers on best practices for large-scale training parallel GPU programming and production ML architecture.

YOUR SKILLS MATTER

• Expertise in Deep Learning Modeling: Demonstrated ability to innovate in model design: embeddings deep MLPs or hybrid architectures or proven experience with sequence models like LSTMs GRUs and Temporal Convolutional Networks
• Expertise in Scalable ML Engineering: Experience building and deploying large-scale ML systems with 50M data points or terabyte-scale datasets leveraging distributed training and GPU acceleration.
• Proficiency in ML and Parallel Frameworks: Strong experience with PyTorch or TensorFlow for large-scale model development and training. Skilled in optimizing performance across multi-GPU or distributed environments ensuring efficient training and inference on datasets exceeding 50 million rows.
• Advanced Data Processing Skills: Hands-on experience with PySpark Dask and Polars for distributed data preparation and transformation. Knowledge of Arrow-based columnar computation and vectorized data pipelines.
• Cloud and High-Performance Computing: Proficiency with AWS Kubernetes and containerized GPU workloads (NVIDIA Docker). Experience in orchestrating multi-GPU/multi-node clusters for deep learning.
• Model Lifecycle & MLOps: Familiarity with MLflow Airflow or Kubeflow for managing large-scale model lifecycles experiments and retraining pipelines.
• Strong Foundations in Machine Learning Engineering: Experience in building deploying and maintaining ML models in production environments.
• Communication & Collaboration: Ability to bridge data science and infrastructure ensuring complex models are productionized efficiently and robustly.

WHAT WE OFFER

• Generous Time Off Enjoy 28 days of vacation an additional day off for your birthday and 1 volunteer day per year
• Work-Life Balance & Flexibility Benefit from a hybrid working model flexible working hours and no dress code
• Great Employee Benefits Access discounts on SIXT rent share ride and SIXT along with partner discounts
• Training & Development Participate in training programs external conferences and internal dev & tech talks designed for your personal growth and development
• Health & Well-being Private health insurance to support your well-being
• Additional Perks Enjoy the Coverflex advantage system to enhance your employee experience

Additional Information :

About us:

We are a globally leading mobility service provider with a revenue of 4.00 billion and around 9000 employees worldwide. Our mobility platform ONE combines our products SIXT rent (car rental) SIXT share (car sharing) SIXT ride (taxi ride and chauffeur services) and SIXT (car subscription) giving our customers access to our fleet of 350000 vehicles the services of 4000 cooperation partners and around 5 million drivers worldwide. Together with our franchise partners we are present in more than 110 countries at 2000 rental stations. At SIXT top-tier customer experience and outstanding customer service are our highest priorities. We believe in true entrepreneurship and long-term stability and align our corporate strategy with foresight. Get started with us and apply now!

Remote Work :

No

Employment Type :

Full-time