SIXT
mobility service provider
SeniorMachineLearningEngineer(m/f/d)
Neural analysis suggests this role is
optimal for mid candidates.
“Senior Machine Learning Engineer (m/f/d) at SIXT. Skills: Machine Learning, Deep Learning, Data Engineering, MLOps, Cloud Computing. develop and implement cutting-edge models specifically tailored for price optimization. deploy low latency services in production with the objective to infer optimal bid price”
What You'll Achieve.
infer optimal bid price; maximize out-of-sample generalization; ensure reproducibility, fault tolerance, and cost-efficient scaling; enable efficient GPU utilization; turn ideas into real impact
Industry & Context.
determine optimal prices under capacity constraints; maximize out-of-sample generalization; optimize performance; enable efficient GPU utilization
What They're Looking For.
Must Have
Deep Learning Modeling, Scalable ML Engineering, ML Frameworks & Parallelization, Advanced Data Processing, Cloud & HPC, MLOps & Lifecycle Management
Nice to Have
experience with sequence models such as LSTMs, GRUs, and Temporal Convolutional Networks, experience building and deploying large-scale ML systems handling 50M+ data points or terabyte-scale datasets, experience leveraging distributed training and GPU acceleration, experience optimizing performance across multi-GPU or distributed environments for datasets exceeding 50M rows, knowledge of Arrow-based columnar computation and vectorized data pipelines, experience orchestrating multi-GPU/multi-node clusters for deep learning, foundations in deploying and maintaining ML models in production environments, collaboration between data science and infrastructure teams
What You'll Do.
develop and implement cutting-edge models specifically tailored for price optimization
deploy low latency services in production with the objective to infer optimal bid price
develop advanced regression prediction models to determine optimal prices under capacity constraints
prototype and evaluate novel architectures
conduct large-scale experimentation and simulation to assess model generalization and analyze behavior across geographies
and capacity constraints
design and optimize large-scale model training pipelines
build high-performance data engineering pipelines for terabyte-scale datasets
implement monitoring and continuous optimization
automating feedback loops
and hyperparameter tuning
documenting infrastructure design
mentoring peers on best practices for distributed ML and production-scale architectures
How You'll Work.
Team & Collaboration
work closely with data scientists; collaboration between data science and infrastructure teams; work alongside engineers, product experts, and business teams; open exchange – across teams, disciplines, and borders; team collaboration
Full Job Description
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 advanced regression prediction models to determine optimal prices under capacity constraints, ensuring architectures capture demand elasticity, capacity limits, and temporal stationarity. * You will prototype and evaluate novel architectures, including deep MLPs, transformer-based models for tabular/sequence data, CNNs for image inputs, and embedding-rich hybrids—optimized for accuracy, robustness, interpretability, and transfer learning to maximize out-of-sample generalization. * You will conduct large-scale experimentation and simulation to assess model generalization and analyze behavior across geographies, seasons, and capacity constraints. * You will design and optimize large-scale model training pipelines, leveraging distributed frameworks (PySpark, Dask, Polars) and multi-GPU/multi-node parallelization for efficient deep learning at scale, ensuring reproducibility, fault tolerance, and cost-efficient scaling on AWS. * You will build high-performance data engineering pipelines for terabyte-scale datasets using Polars, Dask, or Spark, implementing columnar storage, memory mapping, and data sharding to enable efficient GPU utilization. * You will implement monitoring and continuous optimization, automating feedback loops, retraining triggers, and hyperparameter tuning (Ray Tune, Optuna), while documenting infrastructure design and mentoring peers on best practices for distributed ML and production-scale architectures. YOUR SKILLS MATTER * Deep Learning Modeling : Demonstrated ability to innovate in model design (embeddings, deep MLPs, hybrid ar
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