Machine Learning Engineer – (Revenue Management & Price Optimization) (m/f/d)
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The role
Join a team of ML experts to design and deploy cutting-edge models for price optimization in a production-grade, low-latency service.
Develop regression models to infer optimal bid prices under capacity constraints while capturing demand elasticity and temporal dynamics.
Prototype architectures including deep MLPs, transformer-based models for tabular/sequence data, CNNs for image inputs, and embedding-rich hybrids for accuracy and generalization.
Lead large-scale experimentation and simulations to evaluate model generalization across geographies, seasons, and capacity limits.
Build scalable data and training pipelines using PySpark, Dask, or Polars, with multi-GPU/multi-node setups on AWS; ensure reproducibility and robust deployment.
Implement monitoring, automated retraining, and hyperparameter tuning (Ray Tune, Optuna), while documenting infrastructure and mentoring peers on best practices.
Develop regression models to infer optimal bid prices under capacity constraints while capturing demand elasticity and temporal dynamics.
Prototype architectures including deep MLPs, transformer-based models for tabular/sequence data, CNNs for image inputs, and embedding-rich hybrids for accuracy and generalization.
Lead large-scale experimentation and simulations to evaluate model generalization across geographies, seasons, and capacity limits.
Build scalable data and training pipelines using PySpark, Dask, or Polars, with multi-GPU/multi-node setups on AWS; ensure reproducibility and robust deployment.
Implement monitoring, automated retraining, and hyperparameter tuning (Ray Tune, Optuna), while documenting infrastructure and mentoring peers on best practices.
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