SIXT
mobility service provider
SeniorMachineLearningEngineer(m/f/d)
“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
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