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Transform the everyday
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 for the science: the rigor, the evidence, and the 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.

As part of our team, you will have

• An attractive remuneration package- Appealing Siemens pension benefits- Access to employee share plans- 30 days of paid vacation and a variety of flexible work schedules that allow time off for you and your family- 2 to 3 days of mobile working per week as a global standard- Up tp 30 days workation per year in certain countries

Since each of over 300,000 team members feels that other benefits are particularly important, and we cannot list our entire benefit portfolio here, you can find more information here.
The individual benefits are subject to regulatory, contractual, or corporate conditions.

You’ll make an impact by

• 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

This is how you'll win us over

• Education- Master´s degree or an equivalent qualification in Computer Science, Machine Learning, or a related field

• Experience & Skills- 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 toolchains- 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

• Preferred Qualifications:- 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 working with globally distributed research, product, or engineering organizations

• Ways of working: You set scientific direction, lead with autonomy, and raise the bar for rigor and collaboration across teams- Languages: business proficiency in English

You are much more than your qualifications, and we believe in the potential of every single candidate. We look forward to getting to know you!

Your individual personality and perspective are important to us. We create a working environment that reflects the diversity of the society and support you in your personal and professional development. Let’s get to know your authentic personality and create a better future together with us. As an equal-opportunity employer we are happy to consider applications from individuals with disabilities.

About us
To transform the everyday, you need to think outside the box. That’s why we at Siemens are looking for innovators who aren’t afraid to push boundaries to join our diverse team of tech experts. Got what it takes? Then help us create lasting, positive change!
www.siemens.de/careers – if you would like to find out more about jobs & careers at Siemens.
FAQ  – if you need further information on the application process.