The HR Coordinator – Payroll & HR Operations will support the HR Analytics and HR Operations teams by coordinating payroll consolidation activities, managing stakeholder communications, maintaining HR data accuracy, and supporting HR projects and reporting processes.

This role is ideal for someone early in their HR career who is highly organized, detail-oriented, comfortable working with HR data and payroll files, and capable of coordinating with multiple stakeholders across HR, Finance, Payroll, and external vendors. The role also provides exposure to HR systems, reporting, process improvement, and automation initiatives.

1. Functional Planning:

• Estimate investments & TEK (Technical capacity), administer Product cost benchmarking to optimize MAT cost, Logistic cost through Supply Chain Network Design (SCND) & organize Business Case evaluation for Platform & relocation projects as per BBM Process Compass
• Implement SE-Agile methdology for New projects, administer collaboration with SE(Simultaneous Engineering) team & monitor KPI's w.r.t Resource, Time, Specification, Cost & Budget

1. Operations Management:

• Develop Process Flow, Line concept, FMEA, Lean manufacturing concepts for all New products & participate in International Production Network (IPN) connect
• Create and maintain Project management documentation as per PS Process Compass.
• Plan and execute capital equipment (MAE) acquisition and installation, including preacceptance/final acceptance/safe launch & administer handover of project for Series manufacturing along with defined communication matrix

1. Technology Management:

• Define technology fields for manufacturing with Core, Key and Standard processes for New products, Plan front loading of Competence development, Establish training framework & standard for assessing Technology Competence level.
• Define RASIC of SE team, improve collaboration with IPN (International Production Network ) & provide Guidance & support in industrialization phase.

We are seeking a highly skilled and motivated AI/ML Researcher to design, develop, and deploy cutting-edge computer vision systems specifically for driver modeling applications. This role is pivotal in enhancing the safety, comfort, and personalization of our next-generation automotive interior monitoring products. The ideal candidate will combine strong research skills with practical engineering experience to translate innovation into production-ready, robust, and reliable driver modeling solutions for the automotive industry.

Key Responsibilities:

• Research & Development for Driver Modeling: Conduct pioneering research to develop novel algorithms and models specifically tailored for critical driver modeling tasks, including:

                 o Domain-Aware Representation Learning: Investigate and develop effective methods for extracting                        robust, semantically meaningful, and interpretable features from multi-modal sensor data. This                                includes leveraging geometric, behavioral cues, alongside modern representation learning                                        techniques, to capture essential aspects of driver state actions and behavior.
                 o Driver State and Behavior Analysis: Advanced detection of driver attributes like Distraction,                                  Drowsiness (including micro-sleeps), Sleep onset, and emotional states. This involves developing                            sophisticated models to understand and predict complex human behavior, leveraging statistical                                methods, information theory, and probabilistic approaches.
                 o Driver Intent and Activity Recognition: Interpreting driver intent, head pose, gaze tracking, eye-                            gaze estimation, and recognizing specific activities (e.g., phone usage, eating, interacting with vehicle                      controls).
                 o 3D Interior Scene and Driver Interaction Understanding: Interpreting the driver's environment and                       their interactions within the vehicle cabin for a holistic understanding.

• Algorithm Implementation & Optimization for Automotive: Design, train, and optimize deep learning models (e.g., CNNs, Transformers, Recurrent Neural Networks) and advanced traditional computer vision algorithms for core perception tasks central to driver modeling, utilizing multi-modal sensor data commonly found in automotive environments (e.g., RGB, IR, Depth cameras). Focus on computational efficiency and real-time performance suitable for embedded systems.
• Automotive-Grade Data & Pipelines: Devise comprehensive data collection, annotation, and augmentation strategies specifically for diverse driver populations and real-world driving scenarios. Manage and curate large-scale, high-quality automotive datasets for model training, validation, and benchmarking.
• Stay abreast of the latest advancements in AI/ML and computer vision research relevant to driver modeling, contributing to the company's IP through patents or publications.