Senior Principal Applied Scientist

Siemens builds the systems the physical world runs on: factories, power grids, buildings, trains, hospitals. Industrial and physical AI is a major opportunity in applied AI, and one of the harder ones to get right. There is a generation of AI-powered products to build.

We are an applied science organization building the science behind them. The work sits at the intersection of machine learning research, real world data, and production systems running in industrial environments.

As Senior Principal Applied Scientist, you set the scientific direction for the pod. You decide which problems are worth solving with ML, what methods to apply, how to evaluate them, and how to move them from a research result to a model that runs in production. You are accountable forthe science:the rigor,the evidence, andthe outcomes.

This is a senior individual contributor role. Your impact comes from owning the hypotheses, the evaluation, and the path from research to production, and from raising the scientific bar across the team.

Set the applied science roadmap for the pod across the core AI capabilities the product depends on, for example multimodal perception, computer vision, language and agentic reasoning, time series modeling, control

Convert product and system requirements into clear research questions, hypotheses, and success metrics

Design and own the evaluation and benchmarking frameworks for generative and predictive models, including offline metrics, online experimentation, and robustness testing in industrial conditions

Lead applied research projects end to end, from literature review and method selection through experimentation, ablation, and productization

Work with engineers to take models into production grade pipelines: data readiness, optimization, inference, observability

Influence architectural and system decisions with scientific evidence and tradeoff analysis

Identify and de-risk scaling challenges: data quality, model drift, latency, throughput, cost, safety

Mentor scientists and engineers on experimentation rigor, reproducibility, and documentation

Champion responsible and trustworthy AI: bias detection, model risk management, human in the loop controls

10+ years in applied machine learning, AI research, or data science, with a track record of models that shipped to production and made an impact

Strong foundation in machine learning theory and practice across training, evaluation, and deployment

Demonstrated experience setting the science direction for a portfolio of work and shipping it through to production with engineering teams

Proficiency in Python and modern ML frameworks and tool chains

Track record of building evaluation and benchmarking that the team can run a roadmap against

Clear written and verbal communication, with the ability to explain complex ML concepts to engineers, product managers, and senior leaders

Experience setting science direction across multiple capability areas at the same time

Experience bringing applied research into real world products in industrial or physical domains: manufacturing, automation, robotics, energy, mobility, infrastructure, healthcare

Breadth across multimodal ML, generative AI, retrieval, agentic workflows, control, or planning

Scientific ML for physical systems: surrogate modeling, operator learning, physics-informed ML, geometry-aware ML, differentiable simulation, AI for semiconductor/EDA

Experience standing up evaluation, online experimentation, or production monitoring as practices, not just for a single model

Publications, patents, open source contributions, or significant internal technology transfers that the field can point to

Experience hiring and mentoring senior or staff level scientists

Experience…