Role Description Siemens Healthineers is seeking a Learning Ecosystem Integration Lead to help shape the future of learning and leadership development across our global organization. In this highly visible role, you will partner closely with the Global Head of Learning & Leadership Development to design, operationalize, and continuously evolve a scalable, enterprise-wide learning ecosystem. You will serve as a critical connector between strategy and execution—bringing together stakeholders, governance, learning operations, technology, and program delivery to create a seamless and impactful learning experience for employees worldwide. This position is ideal for a strategic and collaborative professional who thrives in complex, fast-paced environments and enjoys turning vision into action. Key Responsibilities - Learning Ecosystem Strategy & Integration: - Partner with the Global Head of Learning & Leadership Development to design, implement, and continuously enhance a global learning ecosystem. - Translate strategic objectives into practical frameworks, operating models, and implementation plans. - Identify opportunities to improve learner experience, operational efficiency, and the overall effectiveness of learning solutions. - Drive alignment across learning technologies, processes, governance, and stakeholder groups to create an integrated learning environment. - Stakeholder Partnership & Influence: - Build strong partnerships across HR, Learning, business functions, and external providers. - Influence and align diverse stakeholders around shared priorities and business outcomes. - Facilitate discussions, workshops, and decision-making processes that drive collaboration and progress. - Serve as a trusted advisor and thought partner on learning ecosystem initiatives. - Governance & Community Leadership: - Co-design and operationalize governance structures, including Learning Councils, advisory boards, and working groups. - Foster engagement across a global community of learning professionals and stakeholders. - Establish clear processes, roles, and decision-making frameworks that support long-term success and scalability. - Drive adoption, communication, and knowledge-sharing efforts across the learning network. - Strategic Program Delivery: - Support the end-to-end execution of strategic learning and leadership development initiatives. - Coordinate activities across multiple projects and workstreams, ensuring alignment and momentum. - Navigate ambiguity and evolving priorities while maintaining focus on strategic objectives. - Partner with vendors, implementation teams, and internal stakeholders to address challenges and accelerate delivery. - Operational Excellence: - Balance strategic thinking with hands-on execution to ensure successful implementation. - Review learning solutions, project deliverables, and implementation plans to ensure quality and consistency. - Support learning operations, vendor management, and process improvement initiatives. - Contribute to the continuous optimization of tools, processes, and ways of working across the learning function. Qualifications - Bachelor's degree in Human Resources, Business, Organizational Development, Learning & Development, Education, or a related field. - 10+ years of experience in learning, talent development, organizational development, education, or a related discipline. - Demonstrated success leading or supporting enterprise-wide initiatives in complex, global organizations. - Experience navigating highly matrixed environments and influencing without direct authority. - Proven ability to connect strategic priorities with operational execution and measurable outcomes. - Strong facilitation, stakeholder management, and relationship-building skills. - Excellent written and verbal communication skills in English. Requirements - Experience supporting learning transformation, learning technology, or ecosystem development initiatives. - Experience establishing governance frameworks and leading cross-functional communities. - Additional language capabilities are a plus. Benefits - Medical insurance - Dental insurance - Vision insurance - 401(k) retirement plan - Life insurance - Long-term and short-term disability insurance - Paid parking/public transportation - Paid time off - Paid sick and safe time

• Model Development: Design, train, and optimize innovative machine learning models for LiDAR/camera perception (e.g., object detection/classification, semantic segmentation, tracking). • Develop and coordinate entire ML workflows, covering data pipelines, model training, model metrics, continuous performance instrumentation, and reporting. • Productization: Take ML models and algorithms from initial evaluation and experimentation all the way to product level on the NVIDIA DRIVE AV platform, developing highly efficient product code in C++. • Innovation: Keep track of the latest developments in machine learning, and incorporate techniques that improve platform performance. • Collaborate with LiDAR/camera teams, developers, engineers, and managers to turn complex ideas into reliable solutions for autonomous driving.

• Design, build and deploy production-grade machine learning and AI systems that power customer-facing analytics, automation, and intelligent product experience. • Develop and operationalize end-to-end ML workflows - from data preparation and feature engineering to model training, deployment, monitoring and continuous improvement. • Build scalable AI solutions leveraging LLMs, embeddings, recommendation systems, forecasting models, and other modern ML techniques. • Collaborate with Product and Engineering teams to identify high-impact use cases and deliver seamless AI-powered data products. • Design and evolve semantic data models that enable accurate, consistent, and scalable analytics across the organization. • Mentor engineers, provide technical leadership, and influence architecture and engineering standards across teams.

Role Description As a Senior Machine Learning Engineer on the Data Mining team, your mission is to build the "Brain" of our ML-powered multimodal data mining framework, Omnitag. You will: - Architect and Train Distilled Models: - Design and implement teacher-student model frameworks for multimodal sensor data. - Develop training pipelines for knowledge distillation. - Ensure student models maintain high accuracy while drastically reducing inference latency and memory footprint. - Reinforcement Learning for Data Discovery: - Build RL-based policy learning and reasoning systems for autonomous driving applications. - Implement and scale RL training workflows (e.g., PPO, DQN, actor-critic methods) for simulation and real-world interaction. - Explore reward shaping, environment modeling, and multi-agent RL where applicable. - Optimize Model Deployment for Real-Time Inference: - Collaborate with backend engineers to deploy distilled and RL models into production. - Optimize for latency, throughput, and hardware efficiency across GPU/CPU clusters. - Implement model versioning, A/B testing, and monitoring for performance regressions. - Research and Integrate Agentic Systems: - Explore and prototype agentic workflows for autonomous reasoning, chain-of-thought prompting, and goal-directed behavior. - Integrate such systems into our broader autonomy stack as experimental or production components. - Drive Production Reliability: - Establish patterns for graceful degradation, fault tolerance, and cost optimization. - Operate Omnitag as a mission-critical data platform serving the entire ML organization, with a focus on reliability, debuggability, and operational excellence. - Mentor and Collaborate: - Work closely with ML scientists, data engineers, and autonomy teams to translate research advances into scalable engineering solutions. - Guide junior engineers in best practices for model training, evaluation, and deployment. Qualifications - BS in Computer Science, Machine Learning, or related field, or equivalent professional experience. - 6+ years of hands-on experience in machine learning engineering, with a focus on model post-training, optimization, and deployment. - Strong experience with model distillation or teacher-student training - practical knowledge of loss functions, training strategies, and evaluation of compressed models. - Proven experience with reinforcement learning in production or research settings: policy optimization, reward design, simulation environments, and RL-based reasoning. - Expert-level proficiency in Python and ML frameworks (PyTorch, TensorFlow, or JAX). - Strong software engineering fundamentals: testing, CI/CD, containerization, and system design. - Experience deploying ML models in cloud environments (AWS, GCP, or Azure) and optimizing for inference. - Demonstrated ability to ship production-grade ML systems and mentor team members. Requirements - Demonstrated track record of shipping robust, well-tested, production-grade systems and mentoring junior engineers. Benefits - Medical, dental, vision. - 401k with a company match. - Health saving accounts. - Life insurance. - Pet insurance. - And more. Salary Range $172,000 — $229,000 USD