Machine Learning Engineer (w/m/d)

Machine Learning Engineer/Data Scientist (Trajectory & Behavior Prediction)Experience : 5+ YearsIndustry : Probabilistic Forecasting, MARL, and Risk ScoringRole Summary :We are seeking a high-caliber Machine Learning Engineer / Data Scientist to architect the next generation of predictive models for autonomous behavior and traffic safety. You will bridge the gap between simulation and real-world deployment by implementing Multi-Agent Reinforcement Learning (MARL) frameworks and time-shifted risk scoring mechanisms. This position requires a deep technical understanding of Vehicle Kinematics and the ability to build sophisticated scoring algorithms based on multi-dimensional data severity. Working closely with simulation and data engineering squads, you will ensure that streaming feature pipelines translate into accurate, event-level predictions that drive cooperative vehicle behaviors.LSTMs, Transformers, or Graph Neural Networks) to forecast participant trajectories and behavior patterns in complex traffic environments.- Risk Scoring Architecture : Develop sophisticated risk scoring mechanisms using Time-Shifted Risk Prediction and sliding time windows to identify potential hazards before they materialize.- MARL Framework Implementation : Architect and train Multi-Agent Reinforcement Learning (MARL) frameworks to simulate cooperative and competitive behaviors among autonomous agents.- Partner with simulation teams to integrate high-fidelity ground truth scenarios and curate replayable datasets for continuous model training.- Build and refine scoring algorithms that weight different data dimensions based on the severity and impact of predicted events.- Advanced Performance Evaluation : Feature Pipeline Engineering : Collaborate with data engineers to define low-latency feature pipelines and manage high-throughput streaming inputs for real-time inference.- Behavioral Simulation : Utilize replayable datasets to validate model performance against historical edge cases and diverse traffic scenarios.Technical Requirements :- Applied Data Science : 5+ years of experience in data science, with a proven track record in real-time systems or simulation-based environments.- Deep Learning Frameworks : Expert-level proficiency in Python and advanced libraries including PyTorch or TensorFlow, NumPy, and Pandas.- Probabilistic Modeling : Strong experience in Time-Series Analysis, Bayesian inference, and probabilistic forecasting.- Hands-on understanding of Reinforcement Learning (RL), with specific exposure to multi-agent settings and cooperative behavior training.- Functional understanding of Vehicle Kinematics, intelligent transportation systems (ITS), and spatial-temporal data structures.Preferred Skills :- Spatial-Temporal Analysis : Experience working with Graph Neural Networks (GNNs) for modeling interactions between multiple traffic participants.- Cloud & Edge Deployment : Familiarity with deploying ML models at the edge or within containerized cloud environments for real-time inference.- Understanding of streaming data platforms (e.g., Ability to articulate complex probabilistic results and risk scores to cross-functional stakeholders in simulation and hardware teams. (tech)